Visual localization and mapping in low light conditions

ABSTRACT

A method comprises generating a map comprising day-time features and night-time features, wherein the position of night-time features relative to the day-time features is determined by at least one image captured during twilight. The invention also relates to a corresponding processing unit configured to execute such a method.

RELATED APPLICATIONS

This application is a continuation of PCT/EP2018/079724, filed Oct. 30,2018, which claims priority from European Patent Application 17199772.9,filed Nov. 2, 2017, the entire contents of each of which are herebyfully incorporated herein by reference for all purposes.

The present invention relates generally to localizing a mobile robot ona map and creating the map. The invention also relates to a robotconfigured to carry out such methods.

Autonomous land-based mobile robots have become an active area ofresearch. The trend is to make such robots drive autonomously orpartially autonomously in pedestrian walkways or in road streets, or inboth pedestrian walkways and road streets as well as other indoor andoutdoor environments. These robots can for example be mobile deliveryrobots that can deliver goods from a starting point to their respectivedestination. Such a mobile robot should have the ability to complete itstasks irrespectively of the light conditions of the environment, be itduring the day, evening or night.

For such a task of autonomous or partially autonomous driving of mobilerobots, among others, it is advantageous that the mobile robots have amethod of at least estimating their position in space so that they cancontinue completing their tasks. To estimate its position, the robotmust use some sort of map on which it can place itself. Such a map canbe preloaded on a robot, or it can be obtained by the robot itself. Inthe latter case, the map can be obtained simultaneously with the robotlocalization by the robot (often referred as Simultaneous Localizationand Mapping, SLAM), or the map can first be developed to be used lateras part of localization.

Current state of the art that addresses such a topic includes the use ofcombined data from various sensors, such as cameras, GPS, odometers,etc. Visual features detected from camera images are matched to mappeddata to infer robot location and orientation. If no map is available, amap can be created from the detected visual features and the featurescan be distilled to other forms for mapping, e.g. a three-dimensionallocation of an object can be triangulated from detected visual featuresthat are thought to be multiple observations of a single underlyingreal-world object. However, such visual features can efficiently beextracted from the environments using conventional cameras typicallyduring the day in good light conditions. Most of typical cameras do notperform well on low light conditions, such as during the evenings orduring the night, and thus most of the visual features extracted fromthe environment are not reliable enough to be used for the process oflocalization or mapping of the mobile robot.

As regards localization at night, there is also an article by P. Nelson,W. Churchill, I. Posner and P. Newman, “From dusk till dawn:Localization at night using artificial light sources,” 2015 IEEEInternational Conference on Robotics and Automation (ICRA), Seattle,Wash., 2015, pp. 5245-5252. This article suggests to use artificiallighting for the localization during the night.

While the discussed prior art may be satisfactory in some instances, ithas certain drawbacks and limitations. In particular, with the abovearticle it is not (or only to a very limited extend) possible to createa night map with little additional effort, i.e., with high efficiency.

In light of the above, it is an object of the present invention toovercome or at least alleviate the shortcomings and disadvantages of theprior art. In particular, it is an object of the present invention togenerate a map configured for navigation during the night with littleeffort, i.e., in an efficient manner. These objects are met by thepresent invention.

The present invention is specified in the claims as well as in the belowdescription. Preferred embodiments are particularly specified in thedependent claims and the description of various embodiments.

In a first embodiment, the invention provides a method comprisinggenerating a map comprising day-time features and night-time features.The position of night-time features relative to the day-time features isdetermined by at least one image captured during twilight.

Thus, a map may be generated comprising both day-time features andnight-time features. That is, there may be provided a map (i.e., a twoor three-dimensional data set) that may be used both during daytime andduring night-time. The creation of the map may be particularly simpleand efficient. In particular, only one map may be created by determiningthe relative positions of night-time features and day-time featuresduring twilight. Put differently, instead of having to create a daytimemap and a night-time map, one “universal” map may be created byregistering the relative positions of features visible during day timeand during night-time at twilight, when at least some of the daytimefeatures and at least some of the night-time features are visible. Thismay be a particularly efficient process to generate the map having bothday-time features and night-time features.

In some embodiments, the method can further comprise capturing at leastone image during twilight. This can be advantageous as the image takenat twilight can be used to determine the position of night-time featuresrelative to the day-time features. An image taken at twilight time canbe advantageous since the light condition during twilight allows forboth (at least some of) the night-time features and (at least some of)the day-time features to be visible and easily detectable on an image.

In some embodiments, the method can comprise extracting visual featuresfrom at least one image captured during twilight. The visual featuresextracted from the image can comprise straight lines (also referred astwilight straight lines to emphasize the fact that they are extractedfrom an image captured during twilight). The visual features extractedfrom the image taken at twilight can further comprise urban lights (alsoreferred to as twilight urban lights to emphasize the fact that they areextracted from an image captured during twilight). In such embodiments,commonality can exist between twilight straight lines and day-timefeatures as well as between twilight urban lights and night-timefeatures. That is, the straight lines seen at twilight can be similarwith the straight lines (or in general with day-time features) seenduring the day, i.e., some of the twilight straight lines may coincidewith the day-time features. Additionally, urban lights can be switchedon during twilight and can be extracted from images taken duringtwilight and can coincide with the urban lights (or in general with thenight-time features). Having different types of visual features, such asthe straight lines and the urban lights, in one image can beadvantageous as it can provide an accurate positioning of the straightlines relative to the urban lights (or the urban lights relative to thestraight lines). Furthermore, straight lines may be a reliable andreproducible feature for mapping and localization.

In some embodiments, the commonality between the twilight straight linesand the day-time features can be used to align the twilight visualfeatures with day-time features. That is, on an image captured attwilight straight lines can be extracted. Due to the relatively goodlight conditions the twilight straight lines can coincide with theday-time features, more particularly with straight lines extracted fromthe image taken during the day. This can be advantageous, especially ifinformation regarding the position (such as the position in the realworld or position on a map) of the day-time features is known. Thus,commonality between twilight straight lines and day time features can beused to align twilight straight lines and day time features, whichimplies that information regarding the position (such as the position inthe real world or position on a map) of the twilight straight lines isgained. Even further, the relative position with regard to each other ofall the twilight visual features (i.e. the twilight straight lines, thetwilight urban lights) extracted from an image taken during twilighttime is known, since they can have been extracted from the same image.Thus, the gained information regarding the position (such as theposition in the real world or position on a map) of the twilightstraight lines implies that information regarding the position (such asthe position in the real world or position on a map) of the othertwilight visual features, such as twilight urban lights can be implied.

In some embodiments, the commonality between the twilight urban lightsand the night-time features can be used to align the twilight visualfeatures with night-time features. That is, on an image captured attwilight, urban lights can be extracted. The urban lights can coincidewith the night-time features, more particularly with urban lightsextracted from the image taken during the night. This can beadvantageous, especially if information regarding the position (such asthe position in the real world or position on a map) of the night-timefeatures is known. Thus, commonality between twilight urban lights andnight-time features can be used to align twilight urban lights andnight-time features, which implies that information regarding theposition (such as the position in the real world or position on a map)of the twilight urban lights is gained. Even further, the relativeposition with regard to each other of all the twilight visual features(i.e. the twilight straight lines and the twilight urban lights)extracted from an image taken during twilight time is known, since theycan have been extracted from the same image. Thus, the gainedinformation regarding the position (such as the position in the realworld or position on a map) of the twilight urban lights implies thatinformation regarding the position (such as the position in the realworld or position on a map) of the other twilight visual features, suchas twilight straight lines, can be inferred.

In some embodiments, the map comprising day-time features and night-timefeatures can be generated by adding twilight visual features to theprovided map.

That is, for example, a map may be provided comprising day-time featureswhich provide information regarding the position (such as the positionin the real world or position on the map) of the day-time features. Asdescribed above, the similarity between twilight visual features (moreparticularly the twilight straight lines) and day-time visual featurescan be used to obtain information regarding the position (such as theposition in the real world or position on the map) of the twilightvisual features. Thus, the twilight visual features can be added to themap. Since the twilight visual features, more particularly twilighturban lights, and night-time visual features can comprise commonalities,a map comprising day-time features and night-time features can begenerated. Such a map can be advantageous as it can be used both duringdaytime and night time with less mapping effort than would be if not“merging” the maps, i.e. comprising a map for daytime and a map for useat night time, that is, comprising a map with daytime features on it andanother map with night time features on it.

In a similar manner the twilight visual features can be added to a map,wherein the map can comprise nigh-time visual features. That is, a mapmay be provided comprising night-time features which provide informationregarding the position (such as the position in the real world orposition on the map) of the night-time features. The similarity betweentwilight visual features (more particularly the twilight urban lights)and night-time visual features comprised by the map can be used toobtain information regarding the position (such as the position in thereal world or position on the map) of the twilight visual features.Thus, the twilight visual features can be added to the map. Since thetwilight visual features, more particularly twilight straight lines, andday-time visual features can comprise commonalities, a map comprisingday-time features and night-time features can be generated.

Again, similarly a map comprising day-time features and night-timefeatures can be generated by adding twilight visual features to aprovided map, and wherein the map does not necessarily comprise visualfeatures, such as day-time features and night time features at thebeginning of the process. Twilight visual features can be added to themap, if the position, such as the position in the real world or positionon the map, of the twilight visual features can be known. In oneexample, the twilight visual features are added to the map by a mobilerobot through a simultaneous localization and mapping method (SLAM).

Since the twilight visual features can comprise at least some of theday-time features and at least some of the night-time features, addingtwilight visual features to a provided map can result in a mapcomprising both (at least some of) day-time features and (at least someof) night-time features.

That is, to generate a map comprising day-time features and night-timefeatures, twilight visual features can be added to the map. It may beparticularly efficient to use the twilight visual features since theycan comprise both day-time features (such as straight lines) andnight-time features (such as the urban lights) or at least some of theday-time features and some of the night-time features. Furthermore, inan image taken at twilight the straight lines and the urban lights canappear together (i.e. are visible enough to be identified andextracted). This can provide an accurate relative position between thetwilight straight lines and twilight urban lights, since they can appearin the same image. The accuracy of the relative position between visualfeatures appearing in the same image can be determined, for example, bythe resolution of the image, such as the number of pixels on the image,or by the algorithm used to extract the visual features from the image.However, the relative position with respect to each other of the visualfeatures extracted from the same image is not influenced by thelocalization of the position where the image was captures. Thus,detecting day-time features, such as the straight lines, and night-timefeatures, such as the urban lights, on the same image provides anefficient manner for determining the relative position of such featureswith respect to each other.

Furthermore, by detecting similarity between the twilight visualfeatures extracted from the image captured during twilight and theday-time features on a provided map, the position of the twilight visualfeatures on the map can be determined. Since during twilight most of (orat least some of) the day-time features are visible and can be detected,many or at least some common features can be found between the twilightvisual features and the visual features provided on the map. Thus, anaccurate position of the common features can be determined as it isprovided on the map (it can be assumed that the information provided onthe map is accurate) and as a result the twilight visual featuresextracted from the same image as the common features, can be determined.

In some embodiments, visual features related to a location can be addedto the map by capturing at least one image on the location. Further,visual features or characteristics of the location can be extracted fromthe at least one image. Such visual features can, for example, bestraight lines that can belong to buildings, fences, sidewalks. Thevisual features can also be urban lights, such as artificial lightsources, street lights, illuminated signs or illuminated windows.Further, the location wherein the image is captured can be estimated andsuch location can be associated to the visual features. The visualfeatures associated with information regarding their position (such asthe position in the real world or position on the map) can be added tothe map. Adding visual features to a map can be advantageous as itfacilitates the creation of a map comprising visual features. Forexample, a map comprising daytime features can be created as well as amap comprising night time features or both daytime features and nighttime features. Furthermore, adding visual features to a map can beadvantageous as it facilitates the process of merging maps comprisingdaytime features with maps comprising night time features.

In some embodiments, the visual features added to the map can comprisedaytime features. The day-time features can comprise visual featuresextracted from an image captured during day time or during good lightconditions. The day-time features can comprise patterns on a day-timeimage that have a substantially straight line. For example, they maybelong to road endings, sides of buildings, sides of signs, fences.Adding daytime features to a map can be advantageous as it facilitatesthe creation of a map comprising daytime features. It can also beadvantageous as it facilitates or improves the accuracy of localizationusing the map and the daytime features, especially during good lightconditions, such as during the day.

In some embodiments, the visual features added to the map can comprisenight-time features. The night-time features can comprise visualfeatures extracted from an image captured during night time or duringlow light conditions. The night-time features can comprise urban lights,such as artificial light sources, street lights, illuminated signs orilluminated windows. Adding night-time features to a map can beadvantageous as it facilitates the creation of a map comprisingnight-time features. It can also be advantageous as it facilitates orimproves the accuracy of localization using the map and the night-timefeatures, especially during low light conditions, such as during thenight.

In some embodiments, the visual features added to the map can comprisetwilight visual features. The twilight visual features can comprisevisual features extracted from an image captured during twilight. Thetwilight visual features can comprise twilight straight lines andtwilight urban lights. The twilight visual features can comprise visualfeatures that can be similar to the daytime visual features. Thetwilight visual features can comprise visual features that can besimilar to the night-time visual features. Adding twilight visualfeatures to a map can be advantageous as it facilitates the creation ofa map comprising twilight visual features, or visual features thatcoincide with daytime features and night-time features. It can also beadvantageous as it facilitates or improves the accuracy of localizationusing the map and the twilight visual features. Further, adding twilightvisual features to a map may facilitate the process of merging a mapcomprising daytime features with a map comprising night-time features.In addition, it facilitates the process of adding night-time features toa map comprising daytime features, as well as adding daytime features toa map comprising night-time features.

In some embodiments, an estimation of the location is facilitated bycomparing visual features extracted from at least one image captured onthe location with visual features comprised by the map used to estimatethe location. That is, a map comprising visual features is used forlocalization. Information regarding the position (such as the positionin the real world or position on the map) of the visual featurescomprised by the map is known (such information can be present on themap). For the sake of localization an image of the surrounding on thelocation can be obtained and visual features can be extracted from suchimages. Further, a comparison of the extracted visual features from theobtained image and the visual features on the map can be done. Ifsimilarity between the visual features is found then the knowninformation regarding the position (such as the position in the realworld or position on the map) of the visual features comprised by themap can be used to infer the position of the location where the imagewas captured.

In some embodiments, the estimation of the location is facilitated by atleast one or any combination of at least one GPS sensor, at least onedead-reckoning sensor, at least one accelerometer, at least onegyroscope, at least one time of flight camera, at least one Lidarsensor, at least one odometer, at least one magnetometer, and at leastone altitude sensor.

That is, in order to add visual features to a map, the approximatelocation where such visual features belong to may be determinedbeforehand. For example, on a particular location an image can becaptured. However, the map where the visual features are to be addeddoes not necessarily have to be only regarding the particular location.That is, it can be a map of a particular area, neighborhood, city, stateor even the whole world. Thus, to solve the ambiguity related to whichposition in the map or in the real world the visual features that needto be added to the map belong to, at least one or a combination ofsensors can be used, such as for example GPS sensor. Further, the visualfeatures extracted from an image on location can be compared to visualfeatures provided on the map. If some common features are found by saidcomparison, then the location of the common features can be determined(since it is provided in the map) and as a result the location of theother visual features extracted from the image can be determined too.Because visual data provide an accurate localization method, the visualfeatures can be accurately added to the map.

In some embodiments, the day-time features can comprise a plurality ofstraight lines. The straight lines can be extracted from patterns on animage that have a shape of substantially straight line. Suchstraight-line patterns may belong, for example, to road endings, sidesof buildings, sides of signs, fences or sidewalks. The straight linescan be advantageous as they can be substantially easily extracted froman image, especially during day time or in general during good lightconditions.

In some embodiments, the straight lines can be extracted from an imageby applying an edge detector algorithm on the image, such as for examplethe Canny algorithm. Also, other edge detector algorithms can be used tofacilitate the extraction of straight lines from an image. Further, theextraction of straight lines from an image can be facilitated byapplying a line extracting algorithm on the results of the edge detectoralgorithm. An example of a line detecting algorithm can be the Houghtransform. Also, other line detector algorithms can be used tofacilitate the extraction of straight lines from an image. In someembodiments, a line detector algorithm configured to extract patterns onan image with a substantially straight-line shape, can be applieddirectly on an image to extract straight lines from that image.

In some embodiments, the night-time features can comprise urban lights.The urban lights can belong to switched-on artificial light sources suchas for example street lights, illuminated signs or illuminated windows.In some embodiments, the urban lights can be extracted from at least oneimage by detecting stationary bright spots. The stationary bright spotscan be advantageous as they can belong to stationary or permanentobjects in the real world and thus can be used as reference forlocalization. Further, the urban lights can be advantageous as they canappear on images, especially on images taken at low light conditions, asbright spots and can thus be easily identified or extracted from thatimage. An exemplary method for extracting or identifying the brightspots, i.e. urban lights, on an image can be brightness thresholding.

The night-time features may comprise night-time straight lines. Straightlines, and also the straight lines detectable during the night, may be avery reliable and stable information that can facilitate mapping andsubsequent localization.

In some embodiments of the first embodiment, the method can be used as aSimultaneous Localization and Mapping (also abbreviated as SLAM) method.That is, localization and mapping is performed simultaneously, i.e. mapdata is created at the same time while information regarding theposition (such as the position in the real world or position on the map)is gained. Simultaneous Localization and Mapping can be advantageous asit facilitates the creation (or update or improvement) of maps regardinga location while at the same time localization is performed.

In some embodiments, the twilights can be defined by the sun beinglocated between 0° and 18° below the horizon (which can be referred asastronomical twilight), preferably between 0° and 12° below the horizon(which can be referred as nautical twilight), such as between 0° and 6°below the horizon (which can be referred as civil twilight). It will beunderstood that all the degrees provided herein typically refer to thecenter of the sun.

In some embodiments, the day-time features can be features orcharacteristics or visual features that are visible on (or that can beidentified on or extracted from) images captures during daytime. Daytimecan be defined by the sun being located above the horizon.

In some embodiments, night-time features are features that are visibleon (or that can be identified on or extracted from) images capturedduring night-time. Night-time can be defined by the sun being locatedmore than 6°, preferably more than 12°, such as more than 18° below thehorizon.

In some embodiments, at least one twilight straight line is added to themap, this adding comprises downsizing the at least one image capturedduring twilight to generate a downsized twilight image, and determiningthe at least one twilight straight line by means of the downsizedtwilight image.

In some embodiments, at least one straight line is added to the map, andthis adding comprises downsizing at least one image captured duringdaytime to generate a downsized daytime image, and determining the atleast one straight line by means of the downsized daytime image.

In some embodiments, at least one night-time straight line is added tothe map, and this adding comprises downsizing at least one imagecaptured during night-time generate a downsized night-time image, anddetermining the at least one night-time straight line by means of thedownsized night-time image.

To detect a line from an image (e.g., the straight line or the twilightstraight line), the color and/or intensity of pixels may be used. E.g.,to detect a line, the color and/or intensity on one side of the linetypically is sufficiently different from that of the pixels on the otherside, and the transition between the two kinds of pixels ideally isquite sudden. If instead of the sudden change there is a slow gradientchange, it may be more difficult to detect a line. In such instances,parallel to the normal line detection process, a line detection ondownscaled images may be used. That is, one may resize the image to besmaller and try to detect lines there. Some of the previously too blurrylines will become sharper with resizing, so one can detect them. Anothertype of line that is more easily detected on downscaled images is thejagged line. A typical example of this is a road edge or sidewalk edge,which can be a zig-zag instead of a perfect line. Downscaling the imagemakes those small imperfections disappear and one can detect a line thatis generally straight in such a downsized image more easily.

Because one detects the low-resolution lines from a downscaled image,one does not know precisely where they would appear on the originalimage. For example, if a line detector would detect lines with one-pixelprecision, then if used on a 3× smaller image, the detected linelocation on the original image would be known with a 3-pixel precision.

In general, such “low resolution lines” are less precise and thus lessuseful. However, low resolution lines are often edges of large objects,such as roads, so they can be quite useful to at least a somewhataccurate localization solution even if there are not enoughhigh-resolution lines. Low light conditions typically produce fewhigh-resolution lines, not just because of lack of visible lines, butalso because the motion blur in the images due to the increased cameraexposure time. This means that low resolution lines can be quite useful,especially also during twilight and during the night.

In some embodiments, the map further comprises information regarding therelative position of the night-time visual features and day-time visualfeatures relative to the Earth, such as, relative to any of the centerof the Earth, Equator and Prime meridian. That is, the visual featurescomprised in the map, are features extracted from a certain location.Information regarding the relative position of this location and theEarth can be comprised by the map. ISO 6709 Standard Representation ofGeographic Point Location by Coordinates can be used to represent therelative position of the night-time visual features and day-time visualfeatures relative to the Earth.

In some embodiments, the map further comprises information related tothe position of any of the roads and buildings of a location. In someembodiments, the map comprises information regarding the relativeposition of the roads and building relative to the day-time features andnight-time features. That is, the map can comprise a representation ofthe roads and/or buildings of the mapped area, associated with theirposition on the area and/or relative position of the roads and/orbuilding relative to day-time features and night-time features. Furtherthe roads and the buildings can be associated with tags, that canrepresent for example the name of the road, address of a building. Theinformation related to the position of any of the roads and buildings ofa location can be advantageous as it can facilitate the localizationusing said map.

In some embodiments, the generation of a map comprising day-timefeatures and night-time features can be facilitated by a mobile robotdriving at a location and extracting any of day-time features andnight-time features from images captured at that location.

In some embodiments, one or more mobile robot can drive at one or morelocations and can generate a map comprising day-time features andnight-time features of each location.

In some embodiments, a map of a location can be extended with a map ofanother location if the relative position between the first and thesecond location is known.

In some embodiments, a map of a location can be extended with a map ofanother location if the maps overlap each other, such that, the mapscomprise common visual features.

Embodiments of the present invention also relate to a method for mappingfeatures, the method comprising: based on at least one image comprisingthe features, generating a first hypothesis for locations of thefeatures by taking into account a subset of the features; and generatinga second hypothesis for locations of the features by taking into accountthe first hypothesis and features that are not included in the subset offeatures, and mapping the features according to the second hypothesis.

The subset of features may not comprise features comprising a clustercharacteristic below a cluster threshold. That is, the subset offeatures may only comprise features comprising a cluster characteristicabove or equal to a cluster threshold. In other words, a feature may bepart of a cluster if the cluster characteristic is below a clusterthreshold value.

The cluster characteristic may be a minimum pixel distance of a featureto another feature and the cluster threshold may be a pixel threshold.

Thus, a feature may be considered part of a cluster if the pixeldistance to the closest feature is below a cluster threshold expressedin pixels. It should be understood that minimum pixel distance denotesthe pixel distance of a feature to the closest other feature.

The cluster characteristic may be a minimum angular distance of afeature to another feature and the cluster threshold may be an anglethreshold. The angular distance between two features can be defined asthe angle between a vector from an origin and directed towards the firstfeature and another vector from the same origin and directed towards theother feature. The origin (wherein the vectors or lines to the featuresco-originate or intersect) can be the center of the camera that capturesthe image wherein the features are extracted.

That is, a feature may be considered part of a cluster if the smallestangle of all angles created between a vector directed towards thefeature and the other vectors directed toward the other features, isbelow a cluster threshold angle, wherein all vectors originate at thesame origin, such as, the center of the camera that captures the imagefrom which features are extracted.

The cluster characteristic may be a minimum real-world distance betweenthe physical object corresponding to a feature and the physical objectcorresponding to another feature and the cluster threshold may be areal-world distance threshold. It will be understood that the real-worlddistance is typically the Euclidean distance.

That is, a feature may be considered part of a cluster if the distancein the real world between the corresponding physical object and theclosest physical object for which a feature is extracted is below acluster threshold value.

The features may be point features, such as light sources.

The method for mapping features may also comprise iteratively generatingadditional hypotheses, wherein for each hypothesis n, the hypothesis nis generated by taking into account hypothesis n−1 and features that arenot included in a subset of the features for the hypothesis n−1. Thatis, the above described rationale of first creating a first hypothesisby only taking into account some features and then expanding thefeatures that are taken into account to create a second hypothesis mayalso be employed iteratively. When performing such iterations, anyhypothesis typically takes into account the previous hypothesis and anexpanded set of features.

In some embodiments employing a cluster threshold, the cluster thresholdmay be iteratively lowered for the additional hypotheses. That is, eachhypothesis (except for the first hypothesis) may be generated by takinginto account the previous hypothesis and a set of features defined bythe features being equal to or exceeding a cluster threshold, whereinthe cluster threshold becomes increasingly smaller. Thus, in eachiteration, fewer features are excluded and more features are consideredthan in the previous iteration.

That is, embodiments of the present invention also relate to creating amap comprising features (e.g., adding the features to a pre-existingmap) in a two-step process. In a first “coarse” step, features are addedto a map. In this first step, for determining the location of thefeatures (and to thus add these features “correctly” to the map), only asub-set of features is taken into consideration, while other featuresare disregarded. Consider, e.g., the situation of mapping night featuressuch as light sources. Such light sources emitting light may be presenton an image (or a plurality of images) captured by a robot. These lightsources may be distributed on the image. Thus, relatively isolated lightsources (i.e., features) may exist and light sources may be arranged ata high density, i.e., in a cluster. Based on the light sources (or, moregenerally, the features) arranged in clusters, it may be more difficultto arrive at correct conclusions, as there may be different possibleconfigurations of such light sources that may result in similar images.In light thereof, embodiments of the present technology employ atwo-stage process (or generally, a multi-stage process, as additionalsteps are not prohibited) for the mapping process. In a first “coarse”step, the clustered features may be disregarded. Thus, in this firststep, one already arrives at an initial hypothesis for the location ofthe features. Then, in a second step, this hypothesis is refined by alsotaking into account the clustered features. Thus, all features may betaken into account for mapping of the features, thus arriving at resultsthat may be more valid compared to not taking into account the clusteredfeatures, while also allowing for an efficient computation, as theclustered features are only taken into account after generating thefirst hypothesis.

Further embodiments of the present invention also relate to a method formapping features, the method comprising: based on at least one imagecomprising the features, assigning a weight to each of the features,wherein the weights are different for at least two features; and basedon the weights assigned to the features, generating a hypothesis forlocations of the features, and mapping the features according to thehypothesis.

It will be understood that the weights assigned to the at least twofeatures may typically be non-zero.

The features may be line segment features and/or point features, such aslight sources.

The weight assigned to each feature may depend on a minimum distance ofthe respective feature to another feature.

The minimum distance may be a minimum pixel distance.

The minimum distance may be a minimum angular distance, i.e. the anglebetween the two directions (or vectors) originating from an origin, suchas, the observer point (e.g. center of the camera) and pointing towardsthe two features.

The minimum distance may be a minimum real-world distance of thephysical object corresponding to the respective feature to the physicalobject corresponding to another feature.

A greater weight may be assigned to a first feature having a greaterminimum distance to another feature than to a second feature having asmaller minimum distance to another feature.

A weight function for the features that depends on the minimum distanceto another feature may be monotonically increasing with increasingminimum distance to another feature.

The weight function may be strictly increasing with increasing minimumdistance to another feature.

That is, generally, features may be assigned different weights when amap is created by using these features. Generally, features may thushave a differing impact on the map that is created (i.e., on whereindividual features will be located on the map). In particular, a lowerweight may be assigned to features being close to one another (includingfeatures that are arranged in clusters). Again, as described above, forfeatures that are located close to one another, there may be differentoptions of creating hypotheses that fit them well (making such featuresless advantageous for map creation than other features). Thus, moreisolated features may yield more reliable results than features that arearranged in clusters. Thus, the present technology may arrive at resultssuperior to the results that would be generated by assigning the sameweight to every feature.

Further embodiments of the present invention also relate to a method oflocalizing an object, the method comprising: based on at least one imagecomprising features, generating a first localization hypothesis for alocation of an object by taking into account a subset of the features;and generating a second localization hypothesis for the location of theobject by taking into account the first localization hypothesis andfeatures that are not included in the subset of features, and localizingthe object according to the second localization hypothesis.

The subset of features may not comprise features comprising a clustercharacteristic below a cluster threshold. That is, the subset offeatures may only comprise features comprising a cluster characteristicabove or equal to a cluster threshold. In other words, a feature may bepart of a cluster if the cluster characteristic is below a clusterthreshold value.

The cluster characteristic may be a minimum pixel distance of a featureto another feature and the cluster threshold may be a pixel threshold.

Thus, a feature may be considered part of a cluster if the pixeldistance to the closest feature is below a cluster threshold expressedin pixels.

The cluster characteristic may be a minimum angular distance of afeature to another feature and the cluster threshold may be an anglethreshold. The angular distance between two features can be defined asthe angle between a vector from an origin and directed towards the firstfeature and another vector from the same origin and directed towards theother feature. The origin (wherein the vectors or lines to the featuresco-originate or intersect) can be the center of the camera that capturesthe image from which the features are extracted.

That is, a feature may be considered part of a cluster if the smallestangle of all angles created between a vector directed towards thefeature and the other vectors directed toward the other features, isbelow a cluster threshold angle, wherein all vectors originate at thesame origin, such as, the center of the camera that captures the imagefrom which features are extracted.

The cluster characteristic may be a minimum real-world distance betweenthe physical object corresponding to a feature and the physical objectcorresponding to another feature and the cluster threshold may be areal-world distance.

That is, a feature may be considered part of a cluster if the distancein the real world between the corresponding physical object and theclosest physical object for which a feature is extracted is below acluster threshold value.

The features may be point features, such as light sources.

It will be understood that this may have advantages corresponding to theadvantages discussed above for the corresponding mapping method. Thatis, this may allow for a fast and reliable localization of an object(e.g., a robot operating in an environment and capturing images of theenvironment).

Further embodiments of the present invention also relate to a method oflocalizing an object, the method comprising: based on at least one imagecomprising features, assigning a weight to each of the features, whereinthe weights are different for at least two features; and based on theweights assigned to the features, generating a localization hypothesisfor a location of the object, and localizing the object according to thelocalization hypothesis.

It will be understood that the weights assigned to the at least twofeatures may typically be non-zero.

The features may be line segment features and/or point features, such aslight sources.

The weight assigned to each feature may depend on a minimum distance ofthe respective feature to another feature.

The minimum distance may be a minimum pixel distance.

The minimum distance may be a minimum angular distance, i.e. the anglebetween the two directions (or vectors) originating from an origin, suchas, the observer point (e.g. center of the camera) and pointing towardsthe two features.

The minimum distance may be a minimum real-world distance of thephysical object corresponding to the respective feature to the physicalobject corresponding to another feature.

A greater weight may be assigned to a first feature having a greaterminimum distance to another feature than to a second feature having asmaller minimum distance to another feature.

A weight function for the features that depends on the minimum distanceto another feature may be monotonically increasing with increasingminimum distance to another feature.

The weight function may be strictly increasing with increasing minimumdistance to another feature.

Again, it will be understood that this may have advantages correspondingto the advantages discussed above for the corresponding mapping method.That is, this may allow for a more reliable localization method comparedto the situation of assigning the same weights to all the features.

Embodiments of the present invention also relate to the combination ofthe embodiments described before. For example, embodiments of thepresent invention also relate to a method comprising the method formapping features as described before and the method of localizing anobject as described before.

Further, also the method described before mentioning day-time featuresand night-time features may be combined with the discussed mappingmethod and/or with the discussed localization method.

That is, it will be understood that the above described two-step mappingor localization processes (only taking into account a subset of featuresin a first step, but then also taking features outside of the subsetinto account in a second step; and the ones assigning different weightsto the individual features) may be combined with the process of matchingday-time features and night-time features. For example, also in theprocess of matching night-time and daytime features, in a first step,only a sub-set of features may be matched, and in a second step, alsofeatures not present in this sub-set can be matched. Further, also forthe process of matching day-time and night-time features, differentweights can be assigned in the matching process. Thus, the describedmethods can also be combined with one another.

In a further embodiment, a processing unit configured to execute themethod according to any of the embodiments of the first embodiment isdisclosed.

Thus, a processing unit can be configured to generate a map comprisingboth day-time features and night-time features. In some embodiments, theprocessing unit can have access to a map comprising day-time features.Further the processing unit can be configured to add night-time featuresto the map and can determine the relative position of the night-timefeatures with respect to the day-time features by using at least oneimage taken during twilight. The processing unit can execute the methodaccording to any of the embodiments of the first embodiment in anefficient and fast manner. For example, the method according to any ofthe embodiments of the first embodiment can be provided to theprocessing unit in the form of an algorithm written in a computerlanguage. The processing unit can also be advantageous as it can beintegrated in larger systems, such as a mobile robot.

In some embodiments, the processing unit can be configured to extractday-time features from an image.

In some embodiments, the processing unit can be configured to extractnight-time features from an image.

In some embodiment, the processing unit can be part of a mobile robotand can facilitate the mobile robot's navigation and localization. Thatis, the mobile robot can comprise a processing unit configured toprocess images. The processing unit can be a System-on-Chip (SoC),preferably comprising at least one Graphical Processing Unit (GPU) orCentral Processing Unit (CPU) or both. The processing unit can beconfigured to execute an image processing algorithm on images, that canfor example be captured by the mobile robot, for extracting visualfeatures from the images. In some embodiments day-time visual features,such as straight lines, can be extracted from the image(s). Suchstraight lines can be extracted from patterns on the image(s) that havea shape of a substantially straight line. Such straight-line patterns,i.e. straight lines, may belong to road endings, sides of buildings,sides of signs, fences etc. In some embodiments, the visual featuresextracted from the image(s) can comprise night-time visual features,such as urban lights. The urban lights can comprise any artificial lightsource captured on the image(s) such as street lights, illuminated signsor illuminated windows.

That is, the present invention proposes using urban lights forautonomous mobile robot navigation, more particularly, for the processof localization and mapping during low light conditions. That is, duringthe day, or in good lighting conditions, the mobile robot can usestraight lines extracted from the surrounding environment, as such linesdo not change much during different times of the day or differentseasons of the year and thus can be reliable. However, during the night,or in low light conditions, most of the visual features that are visibleduring the day are not visible any more, or only a few of such features,such as the straight lines extracted from the environment, are visible.Thus, the present invention proposes the use of other visual featuresduring the night. Such visual features that become visible during thenight can be artificial light sources, such as street lights,illuminated signs, illuminated windows, etc.

However, the use of other visual features during the night, comes withthe cost of the creation of different map data, as the robot willcomprise map data created in daylight conditions and map data created inlow light conditions. Multiple maps can be merged into one map ifsimilarities between the maps can be detected. However, since such mapsuse different visual features as proposed by the current invention,there are very few similarities between them.

To solve such issues, the present invention presents a method formerging night maps, or maps created during low light conditions usingurban lights, with day maps. The merging of such maps is conducted byusing map data extracted at twilight wherein most of the daylight visualfeatures become visible and also the urban lights are still visible.

To sum up, the present invention features the utilization of urbanlights at low light conditions for localization and mapping, inconjunction with or independent from other features used forlocalizations and mapping. Further the present invention presents amethod of using mapping data gathered during twilight time, to align mapdata from daylight time with map data from night time.

In still other words, the present invention generally relates to amobile robot. More particularly, the invention relates to a mobile robotadapted to use visual information for localization in areas and times oflittle illumination. Furthermore, it relates to merging the localizationmaps created from data collected at vastly different levels ofillumination. Furthermore, the present invention also relates to theutilization of urban lights at night for localization and mapping, inconjunction with or independently from other features used forlocalization and mapping, and/or to the use of twilight time to gathermapping data that would help align data from daytime and data from nighttime.

Below further numbered embodiments of the invention will be discussed.

1. A method comprising:

-   -   generating a map comprising day-time features (1) and night-time        features (2),    -   wherein the position of night-time features (2) relative to the        day-time features (1) is determined by at least one image        captured during twilight.

2. A method according to the preceding embodiment, further comprisingcapturing the at least one image during twilight.

3. A method according to any of the preceding embodiments, furthercomprising extracting visual features from at least one image capturedduring twilight wherein the visual features comprise twilight straightlines (1T) and/or twilight urban lights (2T).

4. A method according to the preceding embodiment, wherein commonalityexist between

-   -   twilight straight lines (1T) and day-time features (1); and    -   twilight urban lights (2T) and night-time features (2).

5. A method according to the preceding embodiment, wherein thecommonality between the twilight straight lines (1T) and the day-timefeatures (1) is used to align the twilight visual features (1T, 2T) withday-time features (1).

6. A method according to any of the preceding embodiments and with thefeatures of embodiment 4, wherein the commonality between the twilighturban lights (2T) and night-time features (2) is used to align thetwilight visual features (1T, 2T) with night-time features (2).

7. A method according to the preceding two embodiments, wherein the mapcomprising day-time features (1) and night-time features (2) isgenerated by adding twilight visual features (1T, 2T) to an initiallyprovided map.

8. A method according to any of the preceding embodiments, whereinvisual features related to a location are added to the map by

-   -   capturing at least one image on the location;    -   extracting visual features from the at least one image;    -   estimating the location and associating the location to the        visual features; and    -   adding the visual features associated with respective location        to the map.

9. A method according to the preceding embodiment, wherein the visualfeatures added to the map comprise day-time features (1).

10. A method according to any of the preceding embodiments and with thefeatures of embodiment 8, wherein the visual features added to the mapcomprise night-time features (2).

11. A method according to any of the preceding embodiments and with thefeatures of embodiment 8, wherein the visual features added to the mapcomprise twilight visual features (1T, 2T).

12. A method according to any of the preceding embodiments and with thefeatures of embodiment 8, wherein the estimation of the location isfacilitated by comparing visual features extracted from at least oneimage captured on the location with visual features comprised by the mapused to estimate the location.

13. A method according to any of the preceding embodiments and with thefeatures of embodiment 8, wherein estimation of the location duringdaytime is facilitated by day-time features (1).

14. A method according to any of the preceding embodiment and with thefeatures of embodiment 8, wherein estimation of the location during lowlight conditions is facilitated by night-time features (2).

15. A method according to any of the preceding embodiments and with thefeatures of embodiment 8, wherein the estimation of the location isfacilitated by at least one or any combination of

-   -   at least one GPS sensor, at least one dead-reckoning sensor, at        least one accelerometer, at least one gyroscope, at least one        time of flight camera, at least one Lidar sensor, at least one        odometer, at least one magnetometer, and at least one altitude        sensor.

16. A method according to any of the preceding embodiments, wherein theday-time features (1) comprise a plurality of straight lines (1).

17. A method according to the preceding embodiment, wherein the straightlines (1) are extracted from patterns on images that have a shape of asubstantially straight line.

18. A method according to any of the preceding embodiments and with thefeatures of embodiment 16, wherein the straight lines (1), belong tostationary objects such as road endings, sides of buildings, sides ofsigns, fences.

19. A method according to any of the preceding embodiments and with thefeatures of embodiment 16, wherein the straight lines (1) are extractedfrom an image by

-   -   applying an edge detector algorithm on the image; and    -   applying a line extracting algorithm on the results of the edge        detector algorithm.

20. A method according to the preceding embodiment, wherein the edgedetecting algorithm is the Canny algorithm.

21. A method according to any of the preceding embodiments and with thefeatures of embodiment 19, wherein the line extracting algorithm is theHough transform.

22. A method according to any of the preceding embodiments, wherein thenight-time features (2) comprise urban lights (2).

23. A method according to the preceding embodiment, wherein the urbanlights (2) belong to switched-on artificial light sources captured onimages.

24. A method according to any of the preceding embodiments and with thefeatures of embodiment 22, wherein the urban lights (2) are extractedfrom at least one image by detecting stationary bright spots.

25. A method according to any of the preceding embodiments, wherein thenight-time features comprise night-time straight lines (1N).

26. A method according to any of the preceding embodiments, wherein themethod is used as a Simultaneous Localization and Mapping (SLAM) method.

27. A method according to any of the preceding embodiments, whereintwilight is defined by the sun being located between 0° and 18° belowthe horizon, preferably between 0° and 12° below the horizon, such asbetween 0° and 6° below the horizon.

It will be understood that all the degrees provided herein typicallyrefer to the center of the sun.

28. A method according to any of the preceding embodiments, whereinday-time features (1) are features that are visible on images capturedduring daytime, wherein daytime is defined by the sun being locatedabove the horizon.

29. A method according to any of the preceding embodiments, whereinnight-time features (2) are features that are visible on images capturedduring night-time, wherein night-time is defined by the sun beinglocated more than 6°, preferably more than 12°, such as more than 18°below the horizon.

30. A method according to any of the preceding embodiments and with thefeatures of embodiment 4, wherein at least one twilight straight line(1T) is added to the map, and wherein this adding comprises

-   -   downsizing the at least one image captured during twilight to        generate a downsized twilight image, and determining the at        least one twilight straight line by means of the downsized        twilight image.

31. A method according to any of the preceding embodiments and with thefeatures of embodiment 16, wherein at least one straight line (1) isadded to the map, and wherein this adding comprises

-   -   downsizing at least one image captured during daytime to        generate a downsized daytime image, and determining the at least        one straight line (1) by means of the downsized daytime image.

32. A method according to any of the preceding embodiments and with thefeatures of embodiment 25, wherein at least one night-time straight line(1N) is added to the map, and wherein this adding comprises downsizingat least one image captured during night-time generate a downsizednight-time image, and determining the at least one night-time straightline by means of the downsized night-time image.

33. A method according to any of the preceding embodiments, wherein themap further comprises information regarding the relative position of thenight-time visual features (2) and day-time visual features (1) relativeto the Earth, such as, relative to any of the center of the Earth,Equator and Prime meridian.

34. A method according to any of the preceding embodiment, wherein theISO 6709 Standard Representation of Geographic Point Location byCoordinates is used to represent the relative position of the night-timevisual features (2) and day-time visual features (1) relative to theEarth.

35. A method according to any of the preceding embodiments, wherein themap further comprises information related to a position of any of roadsand buildings of a location.

36. A method according to the preceding embodiment, wherein the mapcomprises information regarding the relative position of the roads andbuilding relative to the day-time features (1) and night-time features(2).

37. A method according to any of the preceding embodiments, wherein thegeneration of map comprising day-time features (1) and night-timefeatures (2) is facilitated by a mobile robot driving at a location andextracting any of day-time features (1) and night-time features (2) fromimages captured at that location.

38. A method according to the preceding embodiment, wherein one or moremobile robot drive at one or more locations and generate a mapcomprising day-time features (1) and night-time features (2) of eachlocation.

39. A method according to the preceding embodiment, wherein a map of alocation can be extended with a map of another location if the relativeposition between the first and the second location is known.

40. A method according to the preceding embodiment, wherein a map of alocation can be extended with a map of another location if the mapsoverlap each other, such that the maps comprise common visual features(1, 2).

Below, methods of mapping features according to embodiments of theinvention will be discussed. These embodiments are abbreviated with theletter “M” followed by a number and referred to as mapping embodiments.

M1. A method for mapping features, the method comprising

-   -   based on at least one image comprising the features, generating        a first hypothesis for locations of the features by taking into        account a subset of the features,    -   generating a second hypothesis for locations of the features by        taking into account the first hypothesis and features that are        not included in the subset of features, and mapping the features        according to the second hypothesis.

M2. The method according to the preceding embodiment,

-   -   wherein the subset of features does not comprise features        comprising a cluster characteristic below a cluster threshold.

M3. The method according to the preceding embodiment,

-   -   wherein the cluster characteristic is a minimum pixel distance        of a feature to another feature and wherein the cluster        threshold is a pixel threshold.

M4. The method according to any of the preceding mapping embodiments andwith the features of embodiment M2,

-   -   wherein the cluster characteristic is a minimum angular distance        of a feature to another feature and wherein the cluster        threshold is an angle threshold.

The angular distance between two features is the angle created between afirst and a second vector (or segment, or line) comprising the sameorigin (or intersecting at a point) and wherein the first vector isdirected towards the first feature and the second vector is directedtowards the second feature. It will be understood that the origin maytypically be a camera (e.g., mounted on a robot) capturing the imagecomprising the features.

M5. The method according to any of the preceding mapping embodiments andwith the features of embodiment M2,

wherein the cluster characteristic is a minimum real-world distancebetween the physical object corresponding to a feature and the physicalobject corresponding to another feature and wherein the clusterthreshold is a real-world distance threshold.

M6. The method according to any of the preceding mapping embodiments,wherein the features are point features, such as light sources.

M7. The method according to any of the preceding mapping embodiments,wherein the method comprises

-   -   iteratively generating additional hypotheses, wherein for each        hypothesis n, the hypothesis n is generated by taking into        account hypothesis n−1 and features that are not included in a        subset of the features for the hypothesis n−1.

M8. The method according to the preceding embodiment and with thefeatures of embodiment M2, wherein the cluster threshold is iterativelylowered for the additional hypotheses.

M9. A method for mapping features, the method comprising

-   -   based on at least one image comprising the features, assigning a        weight to each of the features, wherein the weights are        different for at least two features,    -   based on the weights assigned to the features, generating a        hypothesis for locations of the features, and mapping the        features according to the hypothesis.

M10. The method according to the preceding embodiment, wherein thefeatures are line segment features and/or point features, such as lightsources.

M11. The method according to any of the 2 preceding embodiments, whereinthe weight assigned to each feature depends on a minimum distance of therespective feature to another feature.

M12. The method according to the preceding embodiment, wherein theminimum distance is a minimum pixel distance.

M13. The method according to any of the 2 preceding embodiments, whereinthe minimum distance is a minimum angular distance.

M14. The method according to any of the 3 preceding embodiments, whereinthe minimum distance is a minimum real-world distance of the physicalobject corresponding to the respective feature to the physical objectcorresponding to another feature.

M15. The method according to any of the 4 preceding embodiments, whereina greater weight is assigned to a first feature having a greater minimumdistance to another feature than to a second feature having a smallerminimum distance to another feature.

M16. The method according to any of the 5 preceding embodiments, whereina weight function for the features that depends on the minimum distanceto another feature is monotonically increasing with increasing minimumdistance to another feature.

M17. The method according to the preceding embodiment, wherein theweight function is strictly increasing with increasing minimum distanceto another feature.

Below, a localization method according to embodiments of the inventionwill be discussed. These embodiments are abbreviated with the letter “L”followed by a number and referred to as localization embodiments.

L1. A method of localizing an object, the method comprising

-   -   based on at least one image comprising features, generating a        first localization hypothesis for a location of an object by        taking into account a subset of the features,    -   generating a second localization hypothesis for the location of        the object by taking into account the first localization        hypothesis and features that are not included in the subset of        features, and localizing the object according to the second        localization hypothesis.

L2. The method according to the preceding embodiment,

-   -   wherein the subset of features does not comprise features        comprising a cluster characteristic below a cluster threshold.

L3. The method according to the preceding embodiment,

-   -   wherein the cluster characteristic is a minimum pixel distance        of a feature to another feature and wherein the cluster        threshold is a pixel threshold.

L4. The method according to any of the preceding mapping embodiments andwith the features of embodiment L2,

-   -   wherein the cluster characteristic is a minimum angular distance        of a feature to another feature and wherein the cluster        threshold is an angle threshold.

The angular distance between two features is the angle created between afirst and a second vector (or segment, or line) comprising the sameorigin (or intersecting at a point) and wherein the first vector isdirected towards the first feature and the second vector is directedtowards the second feature. It will be understood that the origin maytypically be a camera (e.g., mounted on a robot) capturing the imagecomprising the features.

L5. The method according to any of the preceding mapping embodiments andwith the features of embodiment L2,

-   -   wherein the cluster characteristic is a minimum real-world        distance between the physical object corresponding to a feature        and the physical object corresponding to another feature and        wherein the cluster threshold is a real-world distance        threshold.

L6. The method according to any of the preceding localizationembodiments, wherein the features are point features, such as lightsources.

L7. The method according to any of the preceding localizationembodiments, wherein the method comprises

-   -   iteratively generating additional localization hypotheses,        wherein for each localization hypothesis n, the localization        hypothesis n is generated by taking into account localization        hypothesis n−1 and features that are not included in a subset of        the features for the localization hypothesis n−1.

L8. The method according to the preceding embodiment and with thefeatures of embodiment L2, wherein the cluster threshold is iterativelylowered for the additional localization hypotheses.

L9. A method of localizing an object, the method comprising

-   -   based on at least one image comprising features, assigning a        weight to each of the features, wherein the weights are        different for at least two features,    -   based on the weights assigned to the features, generating a        localization hypothesis for a location of the object, and        localizing the object according to the localization hypothesis.

L10. The method according to the preceding embodiment, wherein thefeatures are line segment features and/or point features, such as lightsources.

L11. The method according to any of the 2 preceding embodiments, whereinthe weight assigned to each feature depends on a minimum distance of therespective feature to another feature.

L12. The method according to the preceding embodiment, wherein theminimum distance is a minimum pixel distance.

L13. The method according to any of the 2 preceding embodiments, whereinthe minimum distance is a minimum angular distance.

L14. The method according to any of the 3 preceding embodiments, whereinthe minimum distance is a minimum real-world distance of the physicalobject corresponding to the respective feature to the physical objectcorresponding to another feature.

L15. The method according to any of the 4 preceding embodiments, whereina greater weight is assigned to a first feature having a greater minimumdistance to another feature than to a second feature having a smallerminimum distance to another feature.

L16. The method according to any of the 5 preceding embodiments, whereina weight function for the features that depends on the minimum distanceto another feature is monotonically increasing with increasing minimumdistance to another feature.

L17. The method according to the preceding embodiment, wherein theweight function is strictly increasing with increasing minimum distanceto another feature.

C1. A method comprising

-   -   the method for mapping features according to any of the        preceding mapping embodiments and    -   the method of localizing an object according to any of the        preceding localization embodiments.

41. The method according to any of the embodiments 1 to 40, wherein themethod further comprises any of the features recited in the precedingmapping embodiments and/or in the preceding localization embodiments.

Below, processing unit embodiments will be discussed. These embodimentsare abbreviated by the letter P followed by a number. Whenever referenceis herein made to processing unit embodiments, these embodiments aremeant.

P1. A processing unit configured to execute the method according to anyof the preceding embodiments.

P2. A processing unit according to the preceding embodiment, configuredto extract day-time features (1), from an image.

P3. A processing unit according to any of the preceding processing unitembodiments, configured to extract night-time features (2) from animage.

P4. A processing unit according to any of the preceding processing unitembodiments, wherein the processing unit is part of a mobile robot andfacilitates the mobile robot's navigation and localization.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an image of a camera with visual features extracted duringday-time;

FIG. 2 shows an image of a camera with visual features extracted duringlow light condition;

FIG. 3 shows an image of a camera with visual features extracted fromday-time with visual features extracted from night time from anexemplary image;

FIG. 4 shows an image with visual features extracted during twilighttime;

FIG. 5 shows an image with visual features extracted during day-time andwith visual features extracted during night time using the visualfeatures extracted from twilight time;

FIG. 6 depicts a robot operating in an environment comprising roads andsidewalks;

FIG. 7 depicts the environment of FIG. 6 with daytime features that maybe extracted from images captured during day time;

FIG. 8 depicts the daytime features captured in FIG. 7;

FIG. 9 depicts the environment of FIG. 6 with night time features thatmay be extracted from images captured during night time;

FIG. 10 depicts the night time features captured in FIG. 9;

FIG. 11 depicts daytime features and night time features captured duringtwilight;

FIG. 12 depicts a map comprising both daytime features and night timefeatures; and

FIGS. 13(a)-13(f) illustrates a method configured for matchingnight-time features extracted from multiple images.

DESCRIPTION OF VARIOUS EMBODIMENTS

In the following, exemplary embodiments of the invention will bedescribed, referring to the figures. These examples are provided to givefurther understanding of the invention, without limiting its scope.

In the following description, a series of features and/or steps aredescribed. The skilled person will appreciate that unless required bythe context, the order of features and steps is not critical for theresulting configuration and its effect. Further, it will be apparent tothe skilled person that irrespective of the order of features and steps,the presence or absence of time delay between steps can be presentbetween some or all of the described steps.

FIG. 1 shows an exemplary image of a camera with visual featuresextracted during day time. FIG. 1 shows a plurality of visual featuresthat can be extracted from an image during day time for localization andmapping purposes. The visual features extracted from an image takenduring day time can be referred throughout the text as day-time visualfeatures 1, or as day-time features 1. The day-time features 1 can bestraight lines 1 that can be extracted from an image. Straight lines 1can belong to permanent objects, such as buildings, fences, sidewalks,and/or transitory objects, such as cars, shadows or other moving trafficparticipants.

The images can be captured by a mobile robot. The mobile robot can befully autonomous or partially autonomous. For example, the autonomylevel of the mobile robot can be between the levels 1 to 5, as definedby the Society of Automotive Engineers (SAE) in J3016—Autonomy Levels.In some embodiments, the mobile robot can be a fully autonomous mobilerobot (i.e. autonomy level 5 according to SAE). That is, the fullyautonomous mobile robot can navigate, drive and execute otherfunctionalities related to its operation on its own without a humanoperator controlling it. In some embodiments, the mobile robot can bepartially- or semi-autonomous (e.g. any of autonomy levels 1 to 4according to SAE). That is, the nearly autonomous mobile robot can insome instance and/or for some functionalities operate on its own and insome other instance and/or other functionalities be assisted by a humanoperator.

Further, the mobile robot can comprise at least one processing unitwhich can be configured to extract visual features from an image. Thevisual features extracted from the images can be used by the mobilerobot for localization and/or mapping.

During its operation, the mobile robot is configured to capture imagesof its surroundings. That is, the mobile robot can comprise at least onecamera. The mobile robot can be configured to capture at least oneimage, preferably a plurality of images of its surroundings. The mobilerobot may be configured to capture images with a predefined frequency,such as every time it is required for the mobile robot to gather datafor localization or mapping. The frequency of capturing such images maybe programmed on the mobile robot or may be communicated to the mobilerobot by a server or a remote operator. Further, the frequency that themobile robot can capture images of its surroundings may depend on thespeed that the mobile robot is moving. For example, when the mobilerobot is moving with high speeds the need for localization and mappingcan be higher and thus the frequency of capturing the images is higherthan the case when the robot is moving with a low speed or is not movingat all. Further, the frequency of capturing such images may also dependon the environment that the mobile robot is operating.

The images of the surroundings of the mobile robot are fed to aprocessing unit. That is the mobile robot can comprise a processing unitconfigured to process images. The images can also be processed in aremote unit outside the mobile robot, such as in a remote server. In thelatter embodiment, the mobile robot can have a communication componentfor uploading data to and downloading data from said server. Theprocessing component can be a System-on-Chip (SoC), preferablycomprising at least one Graphical Processing Unit (GPU) or CentralProcessing Unit (CPU) or both. The processing unit can be configured toexecute an image processing algorithm on the images captured by themobile robot for extracting visual features 1 from an image. In theembodiment of FIG. 1, day-time visual features 1, i.e. straight lines 1,are extracted from the exemplary image. Such straight lines 1 areextracted from patterns on the exemplary day-time image of FIG. 1 thathave a shape of a substantially straight line. Such straight-linepatterns, i.e. straight lines 1, may belong to road endings, sides ofbuildings, sides of signs, fences etc.

The straight lines 1 can be extracted from the image by applying an edgedetecting algorithm, such as for example the Canny algorithm and thenusing a line extracting algorithm on the result of the edge detectingalgorithm. The line extracting algorithm can be the Hough transform.Also, other edge detection algorithms or line detection algorithms canbe used for the purpose of extracting lines from an image. Methods ofextracting straight lines from an image and using them for mapping andlocalization are described in more details in patent applicationsWO2017076928A1 and WO2017076929A1.

The mobile robot can comprise or have access to map data. The map datacan comprise visual features related to the environment the robot iscurrently operating in. The mobile robot can compare the straight lines1 extracted from the visual images it captured with the visual featuresof the map data the robot has access to. That is, the mobile robot cancomprise a memory component for storing said map data. In anotherembodiment, the mobile robot can comprise a communication component forcommunicating to a remote server where the robot can download the mapdata from. From the comparison of the extracted straight lines 1 withthe map data the mobile robot can estimate its position on the map, thatis the mobile robot can localize itself. For example, the map data areconfigured to map a set of environment features, such as visual featuresor straight lines, to their respective position in space. On the otherside, the mobile robot can identify the position of the extracted visualfeatures from the image, i.e. straight lines 1, relative to the mobilerobot. Thus, by comparing the visual features extracted from the imageswith the map data, the mobile robot, in cases where a similarity isfound, can determine its position in the real world.

That is, the map data can correspond to different position possibilitiesof the robot. In one embodiment, the robot can use the Particle Filteralgorithm to evaluate the likelihood of each position being the trueone. The most likely position can provide, within certain errors, thelocalization of the robot at the time the images were taken. Furtherdetails of estimating the pose of the mobile robot using the map dataand visual features are provided in patent applications WO2017076928A1and WO2017076929A1.

Further sensors and/or data, may be used by the mobile robot todetermine the orientation of the mobile robot, such as a gyroscope. Insuch embodiments, the mobile robot not only can determine itslocalization but can determine its pose, that is the robot'slocalization and orientation.

In another embodiment, the mobile robot may not comprise or may not haveaccess to said map data, or the map data do not exist. In such cases themobile robot creates the map data and further or simultaneously canlocalize itself. If localization and mapping is performedsimultaneously, this method is often referred to as SimultaneousLocalization and Mapping (SLAM). The mobile robot can combine theextracted lines to build map data of the area the visual images weretaken in.

In some embodiments, the mobile robot can further extractlocation-related data from at least one further sensor. Said sensor cancomprise, for example, at least one or a combination of at least one GPSsensor, at least one dead-reckoning sensor, at least one accelerometer,at least one gyroscope, at least one time of flight camera, at least oneLidar sensor, at least one odometer, at least one magnetometer and/or atleast one altitude sensor. The mobile robot can then further combinesaid location-related data with the data from the visual images to buildmap data. That is, in some embodiments, the mobile robot can use datafrom its other sensors to obtain an approximate map of an operating areait finds itself in, and then refine this approximate map using theextracted straight lines 1. Alternatively or additionally, the robot canbuild map data using the extracted straight lines 1 and other sensorreadings simultaneously. This is advantageous, as the other sensors'data can serve as a “sanity check”, or as means to make the cameraimages-based map built from the straight lines more precise andreliable.

FIG. 2 shows an exemplary image of a camera with visual featuresextracted during low light conditions. FIG. 2 depicts an exemplary imageof the same environment of FIG. 1, but during low light conditions(e.g., during night-time), and a set of visual features extracted fromthe image. The visual features extracted from an image taken at lowlight conditions, such as taken during the night, may be referred asnight visual features 2, or simply as night features 2. The visualfeatures extracted from an image taken during low light conditions, suchas during the night, can comprise night straight lines 1N which canbelong to permanent objects, such as buildings, fences, sidewalks,and/or transitory objects, such as cars, shadows or other moving trafficparticipants. Note that the night straight lines 1N belong to thecategory of day-time features 1 (refer to FIG. 1), but since they aredetected at low light conditions, such as at night, they are referred asnight straight lines 1N, to make a distinction from the day-timefeatures 1 which are detected at good light conditions.

Further the visual features extracted from an image taken at low lightconditions, such as at night, can comprise night visual features 2, suchas the urban lights 2 shown in FIG. 2 highlighted with an ellipse shapefor human visualization purposes only. The urban lights 2 can compriseany artificial light source such as street lights 2, illuminated signs2, illuminated windows 2, etc.

Put it simply, a plurality of visual features can be extracted from animage. The visual features can be some predefined patterns that can bedetected on an image. When used for localization, a mobile robot forexample, can capture at least one image of the surroundings and canprocess the captured images to extract said patterns from them, i.e. thevisual features. Such patterns can be shapes on the image that aresubstantially straight (i.e. straight lines) or can be lights appearingon the image as bright spots (i.e. urban lights). Logically it can beunderstood that if the image is taken at good light conditions, such asduring the day, the shapes of the objects captured on the images aremore visible and easily to be detected and in contrary, if the image istaken at low light conditions, such as during the night, the lights canbe more visible and easily detected than the shapes of objects. Thus,the visual features extracted from an image comprise day-time features1, such as the straight lines 1 (i.e. substantially straight shapes ofobjects captured on image) and the night time features 2, such as theurban lights 2 (i.e. the bright spots on the images created by lights).On an image taken at good light conditions, such as during the day, theday-time features 1 are more dominant than the night-time features 2 asnormally during the day the urban lights are off and even if they wouldbe on it would be very hard to detect them since they do not appear asbright spots on the image due to good light conditions. On an imagetaken at low light conditions, such as during the night, the urbanlights 2 would be switched on and would appear on images as bright spotsand thus can easily be detected. Even though the lights conditions arebad, still there may be some straight-line patterns detected on theimages taken at low light conditions, that is, night straight lines 1Ncan be detected.

However, the urban lights 2 are the predominant visual features used atlow light conditions.

The night straight lines 1N can be extracted from an image using thesame manner as described for the extraction of straight lines 1extracted from an image taken at day-time, described in FIG. 1. However,due to the low illumination, since the exemplary image of FIG. 2 istaken at low light conditions, such as at night time, the night straightlines 1N detected during night time are usually different from thestraight lines 1 (see FIG. 1) detected during day time (i.e. they belongto different objects or parts of the objects, or they can belong to thesame part of the object but due to low light conditions appear in adifferent position compared to straight lines 1). That is, most or someof the lines that are visible during the day are not visible during thenight or they are barely visible during the night. Thus, they may beinsufficient to be used on their own for localization. For this reason,in the embodiment of FIG. 2 the mobile robot extracts further featuresfrom the image, such as the urban lights 2.

In some embodiments, the urban lights 2 can be extracted from an image,in a similar manner to the one used for the extraction of straight lines1. In some other embodiments, the urban lights 2 can be extracted froman image using brightness thresholding. The urban lights 2 show up inimages as bright spots and thus are relatively easy to detect and locateon an image. Camera parameters may further be configured to decrease thenoise and saturation associated with the fact that the urban lights 2can be much brighter than their surroundings.

Further most of urban lights 2 detected at low light conditions, such asat night, belong to signs, street lights, illuminated windows, which canbe considered as permanent objects, that is they do not change theirposition for a substantially long time such as weeks, months or evenyears. This makes the urban lights 2 a good reference to be used forlocalization and mapping purposes. However, other sources of light maybe detected during the night, said sources being non-static objects,such as the headlights of a car. Such dynamic light sources can beremoved and not taken for reference. This can be done by taking asequence of images at different times. A moving object can create largeblobs on the image compared to the non-moving objects and thus can beclassified by the mobile robot as a moving object and ignored.

The localization and mapping using the night visual features such as theurban lights 2 and the night straight lines 1N is performed by themobile robot in a similar manner to the one depicted and described inFIG. 1 using the day-time visual features 1. Thus, a further descriptionof the localization and mapping using the night visual features 2 isomitted for the sake of brevity.

Put differently, the type of visual features to use depends on theenvironment and light conditions in which the robot operates. Duringdaylight, the robot can use straight lines 1 detected from cameraimages, because the lines do not change much under different times ofday or different seasons of the year. The only exception is night time,or time with low light condition, when most lines visible during the dayare not visible anymore and few additional lines are visible. However,during the dark evening hours, a new type of visual features becomeprominent—artificial light sources, which can also be referred as urbanlights 2. These can be street lights, signs, illuminated windows ofhomes and businesses, etc. The lights show up as bright spots onotherwise mostly dim camera images. Mapping and localization using thesefeatures are analogous to mapping and localization using straight lines.In other words, the mobile robot does not ignore the few lines it seesat night, but lights make up the majority of the useful localizationdata at night. It is noted that lines that are visible at night areoften created by lighting and shadows, so they are not visible duringthe day, i.e., the lines visible during the night may be different fromthe lines visible during the day.

FIG. 3 illustrates visual features extracted from day-time with visualfeatures extracted from night time from an exemplary image. Moreparticularly, FIG. 3 is a night-time image, i.e., an image obtained withlow light conditions, where features visible at night, also referred toas night-time features 2, e.g., night straight lines 1N and lights 2 arevisible. Furthermore, in FIG. 3, also day-time features 1, e.g.,day-time straight lines 1 are shown. It will be understood that theday-time features 1 are usually different from the night-time features2, i.e., the features that can be obtained during the day (or,generally, during good light conditions) are different from the featuresthat can be obtained during the night (or, generally, during low lightconditions).

In FIG. 3 the night straight lines 1N and the urban light 2 aredepicted. Such visual features can be extracted from an image taken atnight time or at low light conditions as depicted and described in FIG.2. Further FIG. 3 shows the day-time straight lines 1. Such visualfeatures can be extracted from an image taken at day-time as depictedand described in FIG. 1.

From FIG. 3 it can be noticed that there is very few or almost nocommonality between straight lines 1 seen at day-time and night straightlines 1N seen at night, since there are very few similarities betweenthe day-time visual features 1 and night time visual features 2.

Put differently, FIG. 3 illustrates the problem of merging maps withdifferent types of features. The problem may arise from the inaccuracyof anchoring a map to real world locations. Multiple maps can be mergedinto one map if similarities between the maps can be detected. In thecase of using different visual features for day-time and night-timemaps, the similarities can be limited to the outputs from other sensors,if present, such as e.g. GPS signal. However, GPS and other sensors canbe inaccurate, resulting in merged maps whose constituent maps are notaligned properly. This kind of inaccurately merged map will createfurther problems when additional data is added to the map, e.g.locations of roads or obstacles. If the additional data is accuraterelative to the objects on the map that were mapped using day-time data,then it will be less accurate relative to the objects that were mappedusing night time data.

To better understand the scenario of FIG. 3 the following example isprovided. The mobile robot comprises two sets of map data: the day-timemap data and the night time map data. The set of day-time map data canbe referred as S_(D) and the set of night time map data can be referredas S_(N). The set of day-time map data may comprise a plurality ofday-time features 1, such as day-time straight lines 1, and the set ofnight time map data may comprise a plurality of night-time features 2,such as night straight lines 1N and urban lights 2. Assume a scenariowherein the two sets of above-mentioned map data comprise similaritiesbetween each other. Such similarities can be for example a subset ofstraight lines 1 taken from the set of day-time visual features and asubset of night straight lines 1N taken from the night-time visualfeatures. Such similarities can be obtained by intersecting the setsS_(D) and S_(N) and can be referred as the common subset C. That isC=S_(D)∩S_(N), i.e. C is the intersection of the set of day-time mapdata and night time map data. In such a case, the robot can use thecommon subset C to align the two sets of map data S_(D) and S_(N), theday-time map data with the night time map data. Confidence that the twosets of map data S_(D) and S_(N) are aligned with each other can beinferred from the fact that the map data within a map are accuraterelative to each other. That is any element from the day map data S_(D)is accurately mapped relative to other elements of day map data S_(D)and any element from the night map data S_(N) is accurately mappedrelative to other element of night map data S_(N). Thus, aligning theelements from S_(D) and S_(N) that are included in C (i.e. aligning thecommon elements), the whole set of day map data is aligned with thewhole set of night map data resulting in an accurate merged map.

However, the day map data comprise very few similarities with the nightmap data since they are created using different visual features. Inday-time map data, the dominant visual features are the straight lines 1while in the night-time map data the dominant visual features are theurban lights 2, and night straight lines 1N, which, however, may notcoincide with day-time straight lines 1. Thus, illustrating with theprevious example again, the intersection C of S_(D) and S_(N), maycomprise very few elements or in a worst case may be an empty set. Thus,the abovementioned map data will be inaccurately merged and misalignedwith each other as it can be seen in FIG. 3. To solve such problem, itcan be advantageous to extend the common set C. That is, extending thecommonality between data and features of two maps can facilitate theprocess of merging the two maps.

In FIG. 3, the mobile robot is localizing itself in low lightconditions, such as at night. The mobile robot can be provided withday-time map data or may have access to a server comprising day-time mapdata, i.e. map created at good light conditions, comprising day-timevisual features 1 e.g. straight lines 1. For the process of localizingitself the mobile robot can take at least one image of its surroundings.From the at least one captured image the mobile robot can extract nightvisual features 2, such as the urban lights 2 and the night straightlines 1N. As mentioned above the mobile robot can comprise or haveaccess to day-time map data. To estimate its position, the mobile robotcan compare the night visual features 2, with the visual featurescomprised by the day-time map data. Since the visual features in theday-time map data are extracted from images taken during day-time, itcan be inferred that there are very few similarities between theday-time visual features 1 and night time visual features 2. Forexample, during day-time most of the urban lights 2 are off and thuswould not be on the image or the straight lines 1 are not that commonduring the night as it becomes harder to detect them on the images takenat low light conditions. So, the mobile robot can estimate its positionusing the very few similarities between the day-time visual features 1and the night time visual features 2 in conjunction or not with the useof other sensors such as GPS. Since the similarities are few and theoutput from sensors such as GPS can be quite inaccurate, the estimationof the location of the mobile robot would not be correct resulting in anoffset between the estimated position and the real one. A rationale forthe offset can be seen on FIG. 3, wherein the straight lines 1 areprojected on the image using the estimated position. It can be seen thatthe day-time features 1 are not properly aligned with the night-timefeatures 2. That is, the commonalities between the day-time features 1and the night-time features 2 may be relatively low. This may result inan offset between maps based on day-time features and based onnight-time features. Such an offset can represent the error that wasdone during estimation of the position of the mobile robot.

Thus, an aspect of the present invention is to generate a map comprisingday-time visual features 1 and night time visual features 2 that areproperly aligned with each other. This can be done by merging two mapscomprising respectively day-time visual features 1, such as straightlines 1, and night time visual features 2 such as urban lights 2 andnight straight lines 1N. The map comprising day-time visual features 1and night time visual features 2 can also be generated by extending anexisting map comprising day-time visual features 1 with night timevisual features 2 or by extending an existing map comprising night timevisual features 2 with day-time visual features 1 or by extending a mapwith day-time visual features 1 and night time visual features 2. Afurther aspect of the invention is to extend the commonality between theday-time map data and the night time map data. This can facilitate thegeneration of a map comprising day-time visual features 1 and night timevisual features 2.

The solution presented herein to the problem of merging map data with noor low commonality between them comprises introducing additional visualdata to the map. More particularly, to better merge day-time maps withnight time maps, further mapping data are collected during twilight whensome of the day-time visual features 1 are detected and some of thenight-time visual features 2 are detected. Because of the higheraccuracy of the visual data compared to other sensors such as GPS, orwheel odometry, etc., the merged map will have a better accuracy thanthe merged map without the use of twilight data. If additional data isadded to the map and the data is accurate relative to the parts of themap created using day-time data, it will be also accurate relative tonight-time data. This will save mapping effort, as the additional datawill need to be attached to just one universal map instead of multipledifferent maps. These concepts are depicted in the following figures,FIG. 4 and FIG. 5.

FIG. 4 shows an image with visual features extracted during twilighttime. FIG. 4 depicts an exemplary image taken at twilight time, that isin a period of time wherein the sunlight is bright enough for (at leastsome of) the straight lines visible during day-time to be detected in asimilar manner that they would be detected during the day, but still itis dark enough for the urban lights, such as the street lights, to be onand easily detected. FIG. 4 may depict an image taken at a time when thesun is less than 18° below the horizon, preferably below 12°, such asbetween 3° to 8° below horizon.

In FIG. 4 visual features are extracted from an image taken at twilighttime, which can also be referred as twilight visual features 1T, 2T,comprising the twilight urban lights 2T and the twilight straight lines1T. It should be understood that the twilight urban lights 2T belong tothe category of the night time visual features 2 and since they aredetected during twilight time they are referred as twilight urban lights2T to distinct them from urban lights 2 detected during night time. Itshould also be understood that the twilight straight lines 1T belong tothe category of day-time features 1 and since they are detected duringtwilight time they are referred as twilight straight lines 1T todistinct them from the straight lines 1 detected during day-time andnight straight lines 1N detected during nigh-time.

The straight lines 1T seen at twilight are similar to the straight lines1 seen during the day. Put differently, at least some of the twilightstraight lines 1T may coincide with the day-time straight lines 1. Thishappens because during twilight the light conditions can be relativelygood, or at least can be better than during the night, which increasesthe chances of straight lines patterns being captured and detected onthe images taken at twilight. Additionally, urban lights 2T can beswitched on during twilight and thus can be extracted from the imagestaken at twilight. That is, some of the night-time features are alsovisible during twilight. Put differently some of the twilight urbanlights 2T may coincide with the night-time urban lights 2. This happensbecause during twilight the lights can still appear as bright spots onimages, at least can appear as bright spots better than they wouldduring the day, which increases the chances of the bright spots (i.e.urban lights) to be captured and detected on an image taken at twilight.

Thus, the twilight map data comprises visual features that are part ofthe day-time map data such as the straight lines 1T that are similarwith the straight lines 1 and also comprises visual features that arepart of the night time map data such as the urban lights 2T that aresimilar with the urban lights 2. The straight lines 1T and the urbanlights 2T extracted from an image taken at twilight time are accuratelymapped relative to each other since they are gathered in a coherentmanner (i.e. they are gathered from the same images, and the error ofpossible misalignment between them corresponds only to the errorassociated with the image processing algorithm used to extract thefeatures from the images, which in most of the cases is very small andcan be neglected). Furthermore, most of the straight lines 1T aresimilar to the straight lines 1, because at twilight time it is brightenough for the camera(s) to capture the same visual features in similarmanner as they would capture them during day-time. Furthermore, most ofthe urban lights 2T extracted at twilight are similar to the urbanlights 2 extracted at night, since it is dark enough and the urbanlights are still on for the cameras to capture the same visual features2 in similar manner as they would capture them during night time. Inother words, the map data gathered at twilight provide the relativepositions between the visual features extracted at day-time and thevisual features extracted at night time. This way the maps can becorrectly merged into one map.

In one embodiment, the mobile robot can comprise or have access to mapdata that comprise day-time visual features 1, such as the straightlines 1. Such map data can also be referred as day-time map. Asdiscussed above it can be advantageous for the mobile robot to extendthe day-time map with night time visual features 2, so that the mobilerobot can use such one general map to localize itself during day andnight. To facilitate such extension, the mobile robot extracts visualfeatures from images taken during twilight time. The reason why twilighttime is chosen, is that during twilight the light conditions are good,or at least good enough for the straight lines 1T extracted from animage taken at twilight to comprise almost all or at least a part of thestraight lines 1 that can be extracted during day-time, or the straightlines 1 that the day-time map comprises. At the same time, the light islow enough for the urban lights to be switched on. Thus, they willappear in the images taken at twilight and can be extracted from themobile robot as visual features.

As depicted in FIG. 4, the mobile robot can extract from the same imagethe straight lines 1T and the urban lights 2T. Note, that detecting thestraight lines and the urban lights in the same image cannot be possibleon images taken during the day or during the night, in the sense thatduring the day there will be very few or none urban lights that areswitched on and thus can be detected on the image and during the nightthe light is low making the straight lines barely detectable on theimage taken at night and thus very few straight lines will be detected(refer to FIG. 1 and FIG. 2). Furthermore, since the urban lights 2T andstraight lines 1T are extracted from the same image or from the same setof images their position relative to each other is correct (or cancomprise a small error associated with the algorithms used to extractsuch visual features from the image or set of images).

The robot can proceed by estimating its position. Since the mobilerobot, due to relatively good light conditions at twilight, was able toextract the straight lines 1T, by comparing them to the day-time mapdata the robot can comprise or have access to, it can estimate a correctself-position. In other words, the day-time map data can comprise theday-time straight lines 1. Comparing the straight lines 1T extractedfrom the twilight image with the straight lines 1 comprised in theday-time map, many similarities can be found (due to the fact that theycomprise similar features as explained above), which results in a goodestimate of the position of the mobile robot. Since the estimatedposition of the mobile robot is accurate or comprise a very small error,the alignment of the day-time map data with the straight lines 1T andthe urban lights 2 is accurate too (refer to FIG. 5). Thus, the mobilerobot can update its map, with the visual features 2T, that is with theurban light 2T to generate a map comprising both straight lines andurban lights that are properly aligned with each other.

In another embodiment, the mobile robot can comprise or have access tomap data that comprise night time visual features 2, such as the urbanlights 2 and the night straight lines 1N. Such map data can also bereferred as night time map. The mobile robot, using a similar methodwith the one described above can extend the night time map with day-timevisual features 1 using visual features extracted from images takenduring twilight.

In yet another embodiment, the mobile robot can comprise or have accessto map data that do not comprise any visual features. Using a similarmethod described above the map can be extended with both day-time visualfeatures 1 and night time visual features 2. The mobile robot canextract location related data from at least one further sensor, such asone or a combination of at least one GPS sensor, at least onedead-reckoning sensor, at least one accelerometer, at least one time offlight camera, at least one Lidar sensor. Using such sensors, the mobilerobot can estimate its position in space at the moment at least one or aplurality of images were taken. Combining the location-related data withvisual features extracted from the images the mobile robot can generatemap data comprising visual features. If the images are taken duringtwilight the visual features can comprise urban lights 2 and straightlines 1. Thus, a map comprising day time and night time visual featuresis generated. Further in a refinement process the mobile robot canimprove the estimation of its position, previously done using the uppermentioned further sensors, by using the new map and possibly arriving ata better estimate of its position and at the same time the visualfeatures on the map can be realigned accordingly.

The generated map is advantageous, as the mobile robot can use only onemap to localize irrespective of the light conditions. During good lightconditions, the similarities between the visual features extracted fromthe captured images and the visual features comprised in the map will behigh because of the similarity between the straight lines comprised inthe both sets of visual features. During low light conditions, thesimilarities between the visual features extracted from the capturedimages and the visual features comprised in the map will be high becauseof the similarity between the urban lights comprised in the both sets ofvisual features.

FIG. 5 shows an exemplary embodiment of an alignment according to thepresent invention of visual features extracted during day-time withvisual features extracted during night time using the visual featuresextracted from twilight time. In FIG. 5 it can be seen that the day timemap data and the night time map data are well aligned with each other.Thus, the mobile robot instead of comprising a plurality of mapsreferring to different times of the day can comprise only one merged mapthat can be used irrespectively of the light conditions, be it duringthe day, evening, night or twilight.

Embodiments of the present invention will now be described with furtherreference to FIGS. 6 to 12.

FIG. 6 depicts the situation where a mobile robot 10 is travelling in areal-world environment. The real-world environment comprises two roads100, 102 that cross at an intersection. Next to the roads 100, 102,there may be provided sidewalks 110, and the robot 10 may typicallytravel on the sidewalks 110. The sidewalks 110 may be located betweenthe roads 100, 102 and houses, which houses are identified by respectivenumbers 1 to 10 in FIG. 6. The robot 10 may be intended to “ship” ordeliver a delivery to a particular house, such as to house 8. Moreparticularly, in the situation depicted in FIG. 6, the robot 10 may beintended to deliver a delivery at a door 80 of house number 8.

To do so, the robot 10 has to “know” when it is at the right location,i.e., in front of house number 8. For doing that, the robot 10 may beequipped or, more generally, may have access to a map, i.e., to a2-dimensional or 3-dimensional representation of the environment therobot 10 is travelling in.

To localize itself on the map, the robot 10 has to sense somecharacteristics or features of its surroundings. Such features orcharacteristics may then be used to determine the robot's location on amap.

As discussed, during daytime, the robot 10 may be configured to captureimages and to derive daytime features from these images. As furtherdiscussed, these daytime features may in particular comprise straightlines 1, as is depicted in FIG. 7. It is noted that some lines 1 in FIG.7 are depicted as dots. However, the skilled person will understand thatthe dots 1 in FIG. 7 represent vertical straight lines. Generallyspeaking, during daytime, the robot 10 may be configured to extract suchstraight lines 1 (or, more generally, day time features) from the imagesit captures.

The features that the robot 10 can extract at daytime, i.e., the daytimefeatures 1, are also depicted in FIG. 8. This figure essentially depictswhich information the robot 10 can extract directly from the images itobtains.

However, it will be understood that this information extracted from theimages is not yet sufficient for the robot to perform its delivery andto operate. To perform deliveries and to operate safely, the robot 10has to be equipped with additional information. Such additionalinformation can be a “map”. The map comprises additional information(e.g., on roads, road crossings, houses, and doors of houses) and theirpositions relative to the daytime features 1. That is, such a map maycomprise all the information depicted in FIG. 7. Thus, when intending todeliver a delivery at door 80 of house 8, the robot 10 “knows” that itneeds to position itself between daytime features 1′ and 1″.Furthermore, with the additional map data, the robot 10 also “knows”that there is a road between daytime features 1-3 and 1-4.

However, when the robot 10 travels during the night, or, more generally,at low light conditions, it may no longer be possible to detect thedaytime features 1. Instead—see FIG. 9—the robot 10 may be able toextract night time features 2 from the images of the surrounding of therobot 10. As discussed, such night time features 2 may in particular beartificial light sources, such as street lights, illuminated windows,and traffic lights. That is, at night time, the robot 10 is able toextract the night time features 2 directly from the images the robot 10captures—that is depicted in FIG. 10.

To be able to use the night time features 2 for a localization of therobot 10, additional features need to be added to the map comprising thenight time features 2. In principle, it would be possible to addadditional features to the “skeleton” night time map depicted in FIG.10. That is, one could add the relative location of the roads, thesidewalks, the houses and doors to the houses to the skeleton mapdepicted in FIG. 10. Thus, one would arrive at a night time map thatcould be used for localizing the robot 10 at night time.

However, it will be understood that this would result in substantialmapping effort. That is, to enable the robot 10 to operate both at daytime and at night time, one would need to create two maps, one fordaytime operation and one for night time operation.

However, embodiments of the present invention lower the mapping effortby “merging” these maps.

The starting point of such embodiments may be a day time map, i.e., amap comprising features that are visible during daytime (also referredto as daytime features 1) and additional information, such asinformation on roads, road crossings, traffic lights, houses, and doors.That is a map similar or identical to the one depicted in FIG. 7. Oneaim of embodiments of the present invention is to equip such a map withnight time features 2. If the location or position of such night timefeatures 2 was known relative to at least some of the daylight features1, one could also determine the relative location or position of suchnight time features 2 relative to the additional information.

To achieve that, images are also captured at twilight, as is depicted inFIG. 11. As discussed, during twilight, at least some of the daytimefeatures 1 and at least some of the night time features 2 may be visibleand detectable by the robot 10. This allows the positions of the nighttime features 2 to be determined relative to the position of the daytime features 1, and thus, also relative to the additional information.

One may thus arrive at a map that can be used both during daytime andnight time with less mapping effort than would be necessary if not“merging” the maps. Such a map comprising all daytime features 1 and allnight-time features 2 is also exemplarily depicted in FIG. 12.

However, it will be understood that the starting point of suchembodiments may also be a night time map, i.e., a map comprisingfeatures that are visible during night time (also referred to as nighttime features 2) and additional information, such as information onroads, road crossings, traffic lights, houses, and doors. That is a mapsimilar or identical to the one depicted in FIG. 9. One aim ofembodiments of the present invention is to equip such a map with daytime features 1. If the location or position of such daytime features 1was known relative to at least some of the night time features 2, onecould also determine the relative location or position of such daytimefeatures 1 relative to the additional information.

The starting point of such embodiments may also be map without visualfeatures, i.e. without daytime features 1 and night time features 2,comprising only additional information, such as information on roads,road crossings, traffic lights, houses, and doors. One aim ofembodiments of the present invention is to equip such a map with visualfeatures. For this, the determination of the relative location orposition of the visual features, i.e. daytime features 1 and night timefeatures 2 with the additional information may be advantageous. In someembodiments, at least one or any combination of at least one GPS sensor,at least one dead-reckoning sensor, at least one accelerometer, at leastone gyroscope, at least one time of flight camera (TOF), at least oneLIDAR sensor, at least one odometer, at least one magnetometer, and atleast one altitude sensor can be used to facilitate the determination ofthe relative the visual features, i.e. daytime features 1 and night timefeatures 2 with the additional information. That is, the mobile robotcan localize itself on the provided map, more specifically, the mobilerobot can be configured to determine the location while (or after orbefore) capturing an image. For example, the mobile robot can use atleast one of the upper mentioned sensors. Or in another example, anoperator can determine the location were the images are captured from.Further, visual features can be extracted from the images. Such visualfeatures can comprise daytime features and/or night time features. Ifthe location or position of such visual features was known relative toat least some of the additional information on the map, one could addsuch visual features to the map.

In general, the order of addition of daytime features and night timefeatures to a map is not a necessity and there might not even be anorder, as the daytime features, the night time features can be addedtogether simultaneously to a map. Additionally or alternatively, the mapwherein the visual features can be added can comprise any type of visualfeatures, or may not comprise visual features at all.

That is, the embodiment described above in conjunction with FIGS. 6 to12 describes the creation of combined maps by first creating the day mapand then adding night time features to it. However, it will beunderstood that this may also be done the other way around: first createnight map and then add day time features. Generally, one or more robotsmay drive in different places and at different times and make a map fromeach individual drive. Some of these maps partially overlap some othermaps. Then all of such maps of one area may be merged together, to thuscreate a master map where each small part of the map includes featuresfrom the constituent maps' features at that location. Adding anotherdrive to such a master map does not just add more localization features,it will “bend and stretch” the map in a general way, e.g., as somedistances between features may be adjusted in view of a new mappingdrive.

Put differently, the order of addition is not a necessity and one mayfirst generate the daytime map and then add night-time features to it orvice versa. Further, one may also start with an “empty” map (i.e., notcomprising any daytime features or night-time features) and thensuccessively add such features to the map.

It will be understood that it is also possible to first create atwilight map (i.e., a map that includes twilight features, whichtypically include day and night features) and then adding either furtherday features, night features or both. That is, first, features that arevisible during twilight (such as some straight lines that may also bevisible during the day and some artificial light sources that are alsovisible during the night) may be added to the map, and their relativeposition may be determined, and this map may then later be equipped withfurther day features (e.g., straight lines) and/or night-time features(e.g., artificial light sources).

In other words, one may add any map to any other map as long as there issufficient overlap between the maps to properly align them, and thepresent technology is directed to create an overlap of day maps andnight maps by using mapping during twilight.

FIGS. 13(a)-13(f) illustrate an embodiment of the present inventionconfigured for matching night-time features 2 between multiple images ofthe same environment. While this Figure relates to matching feature indifferent images (e.g., in different frames), it should be understoodthat corresponding methods can also be employed when mapping features(i.e., determining their location to put the location on a map) or whenmatching features in images with features on a map (i.e., localizing thefeatures and thus a position of, e.g., a robot capturing the images).Furthermore, while FIGS. 13(a)-13(f) primarily are described with regardto night-time features, it should be understood that the describedmethods are independent of that and that they may also be employed withother features.

As discussed, for mapping light sources and/or performing localizationbased on night-time features 2, the robot 10 may capture multipleimages. Furthermore, night-time features 2 can be extracted from thecaptured images. The extracted night-time features 2 mostly belong tocertain physical objects, such as, light-sources 2 (as they may befairly distinguishable during low light conditions). Furthermore,night-time features 2 from multiple images (of the same environment) maybelong to the same physical object and thus it is advantageous to matchthem.

The different images or frames of the same environment may be obtainedby different cameras of the same mobile robot 10, by the same camera buton different times and/or poses of the mobile robot 10, by the samemobile robot 10, but on different passes of the robot on theenvironment, or by different mobile robots 10 passing on theenvironment. In general, the different images of the same environmentmay not be identical (though they may comprise visual features of thesame physical objects). Hence, matching the extracted visual features ofthese images may not be straightforward.

Similarly, for localizing the mobile robot 10 and/or determining theposition of the detected physical objects (i.e., for mapping theobjects), it may be advantageous to perform a matching between thevisual features extracted from at least one image captured by thecamera(s) of the robot 10 and visual features comprised in a map of theenvironment that the robot 10 may comprise or have access to. As thedata obtained by the cameras and/or other sensors and the data comprisedin the map may comprise certain errors, matching of the visual featuresbetween different sets of data may be challenging.

For sake of brevity, only the matching of night-time features 2 amongdifferent images and only localization based on the night time features2 is discussed. However, it should be understood that the samediscussion is also valid if the day-time features 1 are used instead.Furthermore, a method for matching and localizing based on day-timefeatures 1 (referred there as straight lines) is provided in the patentapplications WO2017076928A1 and WO2017076929A1. A similar method asdiscussed in the aforementioned patent applications can be employed ifnight-time features 2, such as, light sources 2, are used for mappingand localization.

It should be further noticed, that the algorithm for mapping andlocalizing based on night-time features 2, will be illustrated visually.However, the algorithm can be configured to operate on quantified dataand parameters.

FIGS. 13(a) and 13(b) depict two different images captured on the sameenvironment at low light conditions. The light sources can beparticularly visible and distinguishable on an image captured at lowlight conditions and thus, the night-time features 2 can be extractedfrom the images. The extracted night-time features 2 are depicted inFIG. 13(c) (corresponding to the image of FIGS. 13(a)) and 13(d)(corresponding to the image of FIG. 13(b)). The visual featuresextracted from the image of FIG. 13(a) are depicted as filled roundshapes and the visual features extracted from the image of FIG. 13(b)are depicted as empty round shapes, for better distinguishabilitybetween the two. Further, the frames of the images are depicted forbetter illustration. Further still, only some of the extractednight-time features 2 are annotated with characters A, B, C, D, E, F, G,H, A′, B′, C′. The annotated night-time features 2 will be used toillustrate the matching of the night-time features 2 between differentimages (or frames). It should be noted that the matching of thenight-time features 2 can be performed on all or any number of thenight-time features 2 (that is, not necessarily only on the night-timefeatures 2 selected and annotated for illustration). Further, it can beunderstood that the pairs A and A′, B and B′, C and C′, E and E′, F andF′, G and G′ and H and H′ belong to the same physical object (i.e. tothe same light source).

In other words, a first set of night-time features 2, depicted in FIG.13(c) can be extracted from a first image (depicted in FIG. 13(a)) andsecond set of night-time features 2, depicted in FIG. 13(d), can beextracted from a second image (depicted in FIG. 13(b)). Some featuresdetected on both images may correspond to the same physical object (e.g.A and A′). On the other hand, some detected night-time features 2 may beartefacts (e.g. night-time feature D on FIG. 13(c)).

Though both images depict approximately the same environment (i.e. partof the physical objects they capture are the same), the two images arenot identical to each other. For example, the focus of the first imageis at a lower height than the focus of the second image. This can becaused because the images may have been captured at different times,from different robot poses, by different cameras, etc.

Thus, as illustrated in FIG. 13(e), the matching of the night-timefeatures 2, cannot be simply done by centering the images with eachother.

The matching of night-time features 2 between the two images and theircorrespondence to a respective physical object can be performed by aniterative matching algorithm. The matching algorithm first calculates amatching of the night-time features 2, i.e. it calculates whichnight-time features 2 correspond to the same physical object orlandmark. The matching can be done based on similarities between thenight-time features 2, such as, size, position, color (of pixelsrepresenting the feature), brightness (of pixels representing thefeature) and/or distance between the matched features. For example, aninitial matching can be A with A′, C with B′. The night-time features Band C′ may be left un-matched (i.e. considered as unique features andthus unique light sources, on respective images).

Further, the algorithm assumes that the latest calculated matching ofthe night-time features 2 is correct. Based on this assumption, anoptimizer algorithm is configured to reduce the error related to thematching between the night-time features 2 on different images. Forthis, a fitness function can be determined that quantifies theprobability that a certain matching between the night-time features 2correct. Then, the optimizer can adjust certain parameters of thefitness function in order to reduce the error of the matching. Forexample, the optimizer itself can be iterative. It can first calculatethe direction of the solution, meaning it figures out that it has toincrease some variables and decrease others to arrive at a bettersolution. It then makes a step in that direction. Then, in the nextiteration, it figures out, which parameters now need to be increased andwhich to be decreased and makes another step. The optimizer also variesstep size, so that when it gets very close to the optimal solution, itadvances in small steps in order to not step over the optimum.

After the optimization step, the iterative matching algorithm mayrealize a further matching of the night-time features 2. On this furthermatching, the worst matchings from the previous iteration (e.g. matchesthat contribute the most on increasing the error according to thefitness function) can be removed and/or new matches may be added and/ormatches may be rearranged. Then, the optimization algorithm is performedagain on the new matching, and so on, until the optimum matching isfound.

In the example depicted in the figure, the iterative matching algorithmmay infer that the night-time features A and A′, B and B′, C and C′,represent actual physical objects while night-time feature D is actuallyan artefact (as it is not present on the second image) and does notcorrespond to any physical object. The iterative matching algorithm maycalculate the optimum matching, which in this example would comprise thematchings of A with A′, B with B′ and C with C′, as depicted in FIG.13(f). Further, the matching algorithm may calculate the number oflandmarks or physical objects (such as, light sources) and may localizeor improve a previous localization of the detected landmarks on anenvironment based on the night-time features 2 and/or predeterminedrobot pose and/or input from other sensors, such as, GPS, odometer,gyroscope and/or accelerometer.

The same algorithm can also be used for matching night-time features 2extracted from an image (or extracted from multiple images and merged asdescribed above), with map data. This can facilitate localization of themobile robot. That is, after obtaining the night-time features 2 theycan be compared with map data. In this case the map data are assumed tobe comprised or accessible by the mobile robot and the map data comprisenight-time features. Once a similarity is found between the map data andthe extracted visual features, the mobile robot's location and pose canbe estimated.

The comparison between the night-time features 2 and the map data can beperformed as discussed above for the matching between night-timefeatures 2 of multiple images. First the iterative matching algorithmperforms an initial matching between night-time features 2 extractedfrom at least one image and night-time features in a map. Then theoptimizer minimizes the errors done during the matching and so on, untilthe best matching is found. That is, the best map which maximizes theprobability of obtaining the extracted night-time features 2 can befound. Based on the found map the position and orientation of the mobilerobot 10 can be inferred.

However, further factors can affect matching a detected light to amapped light. The position uncertainty of the light-source (i.e.physical objects) in the map (i.e. error in the map data) and theuncertainty about the robot location relative to the map should beconsidered. The latter is changing with each loop of optimization. Thatis, firstly non-clustered lights are matched and the robot location isoptimized based on those matches. Thus, the robot location is knownbetter at this stage (i.e. after matching and optimizing robot locationbased on non-clustered features). Further, the cluster threshold can belowered and as a result fewer features are considered to be clustered(thus more features are considered during the optimization), and theoptimizer can be executed again. After N steps, the cluster thresholdmight become zero, so the optimization of the robot location isperformed based on all detected visual features. However, at this stagethe currently known robot location is closer to the true one, hence thealgorithm can converge faster.

In the above two processes (matching night-time features of differentframes and matching night-time features extracted from at least oneimage with a map) the whole set of the extracted night-time features canbe considered at once. That is, the matching is performed for all theextracted night-time features (though some of them may be discarded,e.g. night-time feature D). However, matching night-time features 2 thatare close to each other can be more challenging than matching night-timefeatures 2 that are distant—be it for matching night-time features 2between different frames or night-time features extracted from at leastone image with a map. Night-time features 2 that are close to each-other(i.e. having, in an image, a distance from each other below a thresholdvalue, that may also be referred as cluster threshold) are referred asclusters of night-time features 20, or for simplicity clusters 20. Forexample, night-time features E, F, G and H form a cluster 20. Similarly,night-time features E′, F′, G′ and H′ form a cluster 20.

The threshold value (i.e. cluster threshold) to determine that certainnight-time features 2 can be considered to form a cluster 20 can, forexample, be represented in the form of pixel distance between thefeatures in the images—i.e. a predefined distance in the image measuredin pixels can be used to determine which night-time features 2 form acluster 20. That is, if at least two night-time features 2 are closer toeach other than a certain cluster threshold, these features areconsidered to form a cluster.

Alternatively or additionally, the angular distance between pairs of(night-time) features 2 can be used to determine which night-timefeatures 2 form a cluster 20. The angular distance between twonight-time features 2 can be measured by measuring the angle createdbetween a first and a second vector (or segment, or line) comprising thesame origin, wherein the first vector is directed towards the first(night-time) feature 2 and the second vector is directed towards thesecond (night-time) feature 2. The common origin of the first and thesecond vector can coincide with the center of the robot, or with thecenter of the camera, or with the origin of coordinate system that themobile robot may utilize (e.g. for mapping, localization and/ornavigation). For example, in an image captured by a front camera image,one feature (e.g., a light source) might be on the left of the image andits direction can be calculated to be 5 degrees left of the cameraforward direction, whereas another feature (e.g., a light source) mightbe on the right side of the image and its direction can be calculated tobe 8 degrees to the right of the camera forward direction. Then theangle between the two directions and features is 13 degrees.

In such embodiments, where the angular distance between pairs ofnight-time features 2 is used to determine which night-time features 2form a cluster, the cluster threshold can be expressed as an angulardistance threshold (e.g. in degrees or radians). Hence, if the anglebetween two night-time features 2 is lower than the cluster thresholdangle, then the two night-time features 2 are considered to form acluster 20.

That is, the clusters 20 can be detected based on the pixel distance onthe image between the night-time features 2 and/or the angular distancebetween the night-time features, wherein cluster threshold is used,expressed in pixel distance and/or angle. Using the pixel distancebetween two night-time features 2 to determine if they form a clustercan be advantageous as it can be faster to calculate the pixel distancecompared to the calculation of the angular distance. On the other hand,the angular distance can be more accurate, particularly when dealingwith night-time features 2 extracted from images captured by differentcameras. In such cases, the relative orientation between the two camerascan be used to infer the angular distance between night-time features 2extracted from images captured by the different cameras. Furthermore,the calculation of the angular distance can also facilitate matching ofnight-time features 2 extracted from images of different cameras or thematching of light-sources captured from different cameras. For example,a rotating robot comprising front and side cameras can determine that alight-source that is visible in the front camera in a previous frame isthe same light visible in the left camera in a next frame, based on thenight-time features 2 extracted from the respective images and relativeorientation between the front and the left camera and the angulardistance of the night-time feature 2 (of the light source) on eachimage. For example, consider that in a first position, the angle betweenthe night-time feature 2 of the light source in the image of the frontcamera and the forward direction of the front camera is 0°, and then therobot rotates 90° clockwise to a second position, and the night-timefeature 2 of the same light-source is visible in the left camera in anangle of 0° from the forward direction of the left camera, wherein theforward directions of the front and the left camera are perpendicular.In such a scenario, the robot can infer the detected night-time features2 belong to the same light-source (or physical object) using themeasured angles.

To put it simply, the use of the angular distance can provide moreaccurate results not only for detecting clusters 20 but also formatching visual features extracted from images of different cameras,while on the other hand the use of pixel distance for detecting clusterscan be faster and/or require less computational power. Furthermore, itcan also be understood that when features only have a small differencein their angle, they will also be close together on an image, so thatthe pixel distance is approximately proportional to the angledifference, and may thus also provide acceptable results.

Other metrics for detecting clusters 20 can also be used (though theycan be less useful compared to the pixel distance and angular distance,discussed above).

For example, two night-time features 2 can be considered to form acluster if the Euclidean distance in the real-world between the physicalobjects that night-time features 2 belong to is below a threshold value.This may require that the night-time feature are matched to respectivephysical objects and that the distance between the physical objects isknown (for example, from a map of the physical objects).

However, in some instances two physical objects may be far apart (in thethird dimension not captured by the camera), but aligned (according tothe view of the camera) and thus very hard to distinguish visually in animage. Hence, though the distance (in 3D) between the physical objectsmay be large (thus rendering them as not being part of a clusters) thedistance between the respective visual features extracted from an imageof the physical objects may be small (in the 2D camera images), whichmeans that they should be considered as clusters.

The setting of the threshold value can be set manually, e.g. based onexperiments or collected empirical data and determining which thresholdvalue (or range of values) provide the best results or allow for afaster convergence of the matching of the night-time features 2 betweendifferent images or mapping. The threshold value can be set by takinginto consideration the uncertainty (or error) during the visual featuredetection, as the location of the visual can be determined up to acertain accuracy, mostly limited by the resolution of the cameras usedto capture the images, but also by other factors (e.g. glare fromlight). The threshold value can further be set by taking intoconsideration the uncertainty of the sensors used for determining therobot poses (or movements), as the robot poses or movements aredetermined up to a certain accuracy based on the accuracy of the sensorsused, such as, inertial measurement unit (IMU), odometer, gyroscope,GPS, etc.

When such clusters are present, the process of matching night-timefeatures 2 can be performed in two stages. Firstly, the night-timefeatures 2 that do not form a cluster 20 are matched. In the secondstage the clusters 20 are also considered. However, during the secondstage the iterative matching algorithm and the optimizer are closer tothe optimal solution (as the distant night-time features 2 are alreadymatched). Thus, the matching of the night-time features—be it formatching night-time features 2 between different frames or night-timefeatures extracted from at least one image with a map—can be performedfaster and more accurately at least compared to the case when all thenight-time features 2 are considered at once.

That is, some embodiments of the present technology relate to generatinga map (i.e., mapping) and further embodiments of the present technologyrelate to localizing a robot on such a map (i.e., localization). Bothembodiments relating to mapping and embodiments relating to localizationmay utilize a two-stage process when utilizing clusters of features thatare detected on images.

Again, with reference to FIGS. 13(a) and 13(c), such clusters offeatures may be clusters of light sources (see E, F, G, and H) on imagescaptured at night time or during twilight. When features (e.g., A, B, C,D, E, F, G, H) are obtained and it is intended to add such features to amap (i.e., to determine their location and then use this location to putthe features on the map), a two-stage process may be employed todetermine the location of the features. In a first step, a firsthypothesis for locations of the features A, B, C, D, E, F, G, H may begenerated without taking into account the features E, F, G, H that arearranged in clusters. Again, features may be considered to be arrangedin clusters, if, e.g., their distance on an image is below a threshold.Then, in a second step and based on the first hypothesis, a secondhypothesis for the locations of the features A, B, C, D, E, F, G, H maybe generated by now also taking into account the features E, F, G, Hthat are arranged in clusters.

This may be beneficial, e.g., for the following reasons: Generally, itmay be relatively difficult to map clusters of features, as it may bemore difficult to determine which pixel in an image corresponds to whichfeature if the features are relatively close to one another (i.e., ifthe features are arranged in a cluster). That is, mapping features in acluster may be more prone to errors than mapping features that are notarranged in a cluster, i.e., isolated features. With the presentlydescribed embodiments, the first hypothesis for mapping the features inan image is created by not taking into account the features in clusters.Thus, a first hypothesis is created not taking into account the featuresthat are likely to cause errors. However, these features (arranged inclusters) are then used in second step to refine a second hypothesisbeing more refined than the first hypothesis. By first creating a“coarse” first hypothesis and then refining it with the clusteredfeatures, one may arrive at a viable hypothesis for the location of thefeatures (including the clustered features) a lot faster than would bepossible if already considering the error-prone clustered features inthe first step. Further, by also taking into account the clusteredfeatures, one arrives at a second hypothesis relating to the location ofthe features that is generally more valid than would be the case ifcompletely ignoring the clustered features. Thus, the presentlydescribed embodiments allow a relatively exact mapping of features in arelatively short amount of time.

While in the above, a two-stage process for mapping has been described(creating a first hypothesis not taking into account clustered featuresand then creating a second hypothesis based on the first hypothesis andtaking into account the clustered features), it should be understoodthat a corresponding two-stage process can also be employed forlocalization. That is, after capturing an image, in a first step, afirst hypothesis for a location of a robot can be generated by takinginto account the isolated features only (and not the clusteredfeatures), and in a second step, a second hypothesis for a location ofthe robot can be generated by taking into account the first hypothesisand also the clustered features. Again, this may have advantagescorresponding to the advantages discussed above with regard to thetwo-stage mapping process, i.e., it may allow for a fast and reliablelocalization.

The presently described embodiments primarily relate to localization andmapping at night or during twilight, as in these light conditions, pointfeatures (such as light sources), including clustered point features arenumerous. However, it will be understood that these embodiments are notlimited to such light conditions and that the above described technologymay also be employed during the day and in good light conditions.

Further, generally, embodiments of the present technology may assigndifferent weights to features. This may particularly apply tolocalization and mapping, and the embodiment relating to localizationwill be discussed first. Exemplary reference can again be made to FIG.13(c) depicting different features A to G identified on an image. Whenlocalizing the robot capturing the respective image, the features A to Gneed to be matched to respective features that are present on a map. Inembodiments of the present technology, different weights may be assignedto different features, e.g., feature B may be assigned a weight greaterthan feature A in the matching process. Generally, the more isolated afeature is (e.g., the lower the number of other features in itsvicinity), the greater the weight that is assigned to this feature inthe matching process. Again, isolated features may be more reliablymatched so that it may be advantageous to assign higher weights to moreisolated features to arrive at reliable results. While FIG. 13(c) onlydepicts light sources as features, it will be understood that thisembodiment may be employed independent of the exact features used, i.e.,it may also be employed, e.g., with line segments as features.

Further, this embodiment may also be employed for mapping. When mappingfeatures, their location needs to be determined. Also in this step ofdetermining the location of a feature, embodiments of the presenttechnology may assign different weights to features. E.g., for mappingthe location of features A in FIG. 13(c), all the features A to G may betaken into consideration with different weights, the different weightsagain depending on the level of isolation of a feature (and generallyassigning a higher weight to more isolated features). Again, this mayhave the benefits discussed before.

That is, during mapping of features and localization based on features,clustered features may be assigned a smaller weight compared to isolatedfeatures. Again, as discussed above, the clustered features and isolatedfeatured may be differentiated based on a cluster threshold. The weightassignment to the features can thus allow for a de-prioritization ofclustered features, because, due to their closeness to each-other, itcan be more challenging to match them unambiguously—i.e. determine whichpixel on the image belong to which of the clustered features (duringmapping of the features) and/or determine which physical objectcorresponds to which feature (during localization based on features).Thus, taking clustered and isolated features into account in the samemanner may not be efficient, as the clustered features may introduceerrors and may increase the time required for mapping and localization.On the other hand, not considering the clustered features at all mayalso not be efficient, as they may still provide further information forrealizing an accurate mapping and localization. That is, observingfeatures very close to each-other may still provide useful information,even if it can be challenging to determine which is which. Furthermore,even a wrongly matched feature, e.g. a feature matched to another closefeature within a cluster, may still facilitate finding the optimumsolution (i.e. a correct mapping of the features or a correctdetermination of a location based on the features). That is, asclustered features are close to each-other, the error performed whenwrongly matching them is small, hence they may positively contributeinto finding the optimum matching.

Whenever a relative term, such as “about”, “substantially” or“approximately” is used in this specification, such a term should alsobe construed to also include the exact term. That is, e.g.,“substantially straight” should be construed to also include “(exactly)straight”.

Whenever steps were recited in the above or also in the appended claims,it should be noted that the order in which the steps are recited in thistext may be accidental. That is, unless otherwise specified or unlessclear to the skilled person, the order in which steps are recited may beaccidental. That is, when the present document states, e.g., that amethod comprises steps (A) and (B), this does not necessarily mean thatstep (A) precedes step (B), but it is also possible that step (A) isperformed (at least partly) simultaneously with step (B) or that step(B) precedes step (A). Furthermore, when a step (X) is said to precedeanother step (Z), this does not imply that there is no step betweensteps (X) and (Z). That is, step (X) preceding step (Z) encompasses thesituation that step (X) is performed directly before step (Z), but alsothe situation that (X) is performed before one or more steps (Y1), . . ., followed by step (Z). Corresponding considerations apply when termslike “after” or “before” are used.

While in the above, a preferred embodiment has been described withreference to the accompanying drawings, the skilled person willunderstand that this embodiment was provided for illustrative purposeonly and should by no means be construed to limit the scope of thepresent invention, which is defined by the claims.

I claim:
 1. A method comprising: generating a map comprising daytimefeatures and nighttime features, wherein a position of nighttimefeatures relative to the daytime features is determined by at least oneimage captured during twilight.
 2. A method according to claim 1,further comprising capturing the at least one image during twilight andextracting twilight visual features from the at least one image capturedduring twilight wherein the twilight visual features comprise twilightstraight lines and/or twilight urban lights.
 3. A method according toclaim 2, wherein at least one commonality between the twilight straightlines and the daytime features is used to align the twilight visualfeatures with daytime features.
 4. A method according to claim 2,wherein at least one commonality between the twilight urban lights andthe nighttime features is used to align twilight visual features withnighttime features.
 5. A method according to claim 4, wherein the mapcomprising daytime features and nighttime features is generated byadding to a provided map any of: twilight visual features; nighttimefeatures; and daytime features.
 6. A method according to claim 1,wherein visual features related to a location are added to the map bycapturing at least one image on the location; extracting visual featuresfrom the at least one image; estimating the location and associating thelocation to the visual features; and adding the visual featuresassociated with respective location to the map.
 7. A method according toclaim 6, wherein estimation of the location is facilitated by comparingvisual features extracted from at least one image captured on thelocation with visual features comprised by the map used to estimate thelocation.
 8. A method according to claim 6, wherein estimation of thelocation during daytime is facilitated by daytime features.
 9. A methodaccording to claim 6, wherein estimation of the location during lowlight conditions is facilitated by nighttime features.
 10. A methodaccording to claim 6, wherein the estimation of the location isfacilitated by at least one or any combination of: at least one GPSsensor, at least one dead-reckoning sensor, at least one accelerometer,at least one gyroscope, at least one time of flight camera, at least oneLidar sensor, at least one odometer, at least one magnetometer, and atleast one altitude sensor.
 11. A method according to claim 1, whereinthe daytime features comprise a plurality of straight lines.
 12. Amethod according to claim 1, wherein the nighttime features compriseurban lights.
 13. A method according claim 1, wherein the method is usedas a Simultaneous Localization and Mapping (SLAM) method.
 14. A methodaccording to claim 1, wherein twilight is defined by the sun beinglocated between 0° and 18° below the horizon, preferably between 0° and12° below the horizon, such as between 0° and 6° below the horizon. 15.A method according to claim 14, wherein twilight is defined by the sunbeing located between 0° and 12° below the horizon.
 16. A methodaccording to claim 15, wherein twilight is defined by the sun beinglocated between 0° and 6° below the horizon.
 17. A processing unitconfigured to execute the method of claim
 1. 18. The processing unit ofclaim 17, wherein the processing unit is part of a mobile robot andfacilitates the mobile robot's navigation and localization.